Scriptwriting has traditionally been text-centric, a modality that only partially conveys the produced audiovisual experience. A formative study with professional writers informed us that connecting textual and audiovisual modalities can aid ideation and iteration, especially for writing dialogues. In this work, we present Script2Screen, an AI-assisted tool that integrates scriptwriting with audiovisual scene creation in a unified, synchronized workflow. Focusing on dialogues in scripts, Script2Screen generates expressive scenes with emotional speeches and animated characters through a novel text-to-audiovisual-scene pipeline. The user interface provides fine-grained controls, allowing writers to fine-tune audiovisual elements such as character gestures, speech emotions, and camera angles. A user study with both novice and professional writers from various domains demonstrated that Script2Screen’s interactive audiovisual generation enhances the scriptwriting process, facilitating iterative refinement while complementing - rather than replacing - their creative efforts.

Smart glasses enhance interactions with the environment by using head-mounted cameras to observe the user’s viewpoint, but lack the visual feedback used for common interactions. We introduce “Gazeify then Voiceify”, a multimodal approach allowing object selection via gaze and voice using displayless smart glasses. Users can select a physical object with their gaze, and the system generates a digital mask and a voice description of the object’s semantics. Users can further correct errors through free-form conversation. To demonstrate our approach, we develop an interactive system by integrating advanced object segmentation and detection with a visual-language model. User studies reveal that participants achieve correct gaze selection in 53% of the task trials and use voice disambiguation to correct 58% remaining errors. Participants also rated the system as likable, useful and easy to use.

Joanne Leong, David Ledo, Thomas Driscoll, Tovi Grossman, George Fitzmaurice, and Fraser Anderson. 2025. Paratrouper: Exploratory Creation of Character Cast Visuals Using Generative AI. In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (CHI '25). Association for Computing Machinery, New York, NY, USA, Article 189, 1–20.

AI programming tools enable powerful code generation, and recent prototypes attempt to reduce user effort with proactive AI agents, but their impact on programming workflows remains unexplored. We introduce and evaluate Codellaborator, a design probe LLM agent that initiates programming assistance based on editor activities and task context. We explored three interface variants to assess trade-offs between increasingly salient AI support: prompt-only, proactive agent, and proactive agent with presence and context (Codellaborator). In a within-subject study (N=18), we find that proactive agents increase efficiency compared to prompt-only paradigm, but also incur workflow disruptions. However, presence indicators and interaction context support alleviated disruptions and improved users' awareness of AI processes. We underscore trade-offs of Codellaborator on user control, ownership, and code understanding, emphasizing the need to adapt proactivity to programming processes. Our research contributes to the design exploration and evaluation of proactive AI systems, presenting design implications on AI-integrated programming workflow.

Students often take digital notes during live lectures, but current methods can be slow when capturing information from lecture slides or the instructor's speech, and require them to focus on their devices, leading to distractions and missing important details. This paper explores supporting live lecture note-taking with mixed reality (MR) to quickly capture lecture information and take notes while staying engaged with the lecture. A survey and interviews with university students revealed common note-taking behaviors and challenges to inform the design. We present MaRginalia to provide digital note-taking with a stylus tablet and MR headset. Students can take notes with an MR representation of the tablet, lecture slides, and audio transcript without looking down at their device. When preferred, students can also perform detailed interactions by looking at the physical tablet. We demonstrate the feasibility and usefulness of MaRginalia and MR-based note-taking in a user study with 12 students.

Although the point-and-select interaction method has been shown to lead to user and system-initiated errors, it is still prevalent in VR scenarios.Current solutions to facilitate selection interactions exist, however they do not address the challenges caused by targeting inaccuracy.To reduce the effort required to target objects, we developed a model that quickly detected targeting errors after they occurred.The model used implicit multimodal user behavioral data to identify possible targeting outcomes.Using a dataset composed of 23 participants engaged in VR targeting tasks, we then trained a deep learning model to differentiate between correct and incorrect targeting events within 0.5 seconds of a selection, resulting in an AUC-ROC of 0.9.The utility of this model was then evaluated in a user study with 25 participants that identified that participants recovered from more errors and faster when assisted by the model.These results advance our understanding of targeting errors in VR and facilitate the design of future intelligent error-aware systems.

Advances in artificial intelligence open the possibility of predicting and recommending sequences of GUI commands to a user. An interesting question raised by this capability is how to present such recommendations to the user – as a sequential set of individual command recommendations, or as one aggregated recommendation consisting of multiple commands. In this paper we propose an interface for aggregated command recommendation and conduct controlled studies to compare sequential versus aggregated command recommendation across a range of simulated utility conditions. Our results indicate that aggregated command recommendation can improve overall task performance over sequential recommendation, and that this benefit comes from enabling users to rapidly recognize and use high-utility aggregated recommendations. The aggregated command recommendation approach also reduced deliberation time when evaluating and correcting imperfect sets of recommended commands.

Adaptive training of motor-skills, where the difficulty level of the training task is adapted optimally based on the learner’s skill levels, has been shown to enable higher learning gains compared to non-adaptive training. However, prior approaches rely on adapting physical tools that are tedious to design and build. This work investigates using augmented reality (AR) to achieve a similar objective of maintaining functional task difficulty – the difficulty experienced by the learner – at an optimal challenge point during adaptive training. A study prototype of an AR adaptive basketball training system was developed, wherein the learners train to throw a physical ball into a virtual AR hoop seen through a head-mounted device. Results from the study (N=16) aimed to measure the learning gains showed higher learning gains after adaptive AR training compared to non-adaptive AR training. An analysis of participant feedback, however, highlighted challenges with AR-based adaptive training, pointing to the need for a different design approach compared to the physical adaptive tools. Collectively, this exploratory study investigates the use of AR for adaptive motor-skill learning and lays the foundation for future research directions for the AR-tool design.

Foundation models are rapidly improving the capability of robots in performing everyday tasks autonomously such as meal preparation, yet robots will still need to be instructed by humans due to model performance, the difficulty of capturing user preferences, and the need for user agency. Robots can be instructed using various methods-natural language conveys immediate instructions but can be abstract or ambiguous, whereas end-user programming supports longer-horizon tasks but interfaces face difficulties in capturing user intent. In this work, we propose using direct manipulation of images as an alternative paradigm to instruct robots, and introduce a specific instantiation called ImageInThat which allows users to perform direct manipulation on images in a timeline-style interface to generate robot instructions. Through a user study, we demonstrate the efficacy of ImageInThat to instruct robots in kitchen manipulation tasks, comparing it to a text-based natural language instruction method. The results show that participants were faster with ImageInThat and preferred to use it over the text-based method. Supplementary material including code can be found at: https://image-in-that.github.io/.

We explore the use of multimodal input to predict the landing position of a ray pointer while selecting targets in a virtual reality (VR) environment. We first extend a prior 2D Kinematic Template Matching technique to include head movements. This new technique, Head-Coupled Kinematic Template Matching, was found to improve upon the existing 2D approach, with an angular error of 10.0° when a user was 40% of the way through their movement. We then investigate two additional models that incorporated eye gaze, which were both found to further improve the predicted landing positions. The first model, Gaze-Coupled Kinematic Template Matching resulted in angular error of 6.8° for reciprocal target layouts and 9.1° for random target layouts, when a user was 40% of the way through their movement. The second model, Hybrid Kinematic Template Matching, resulted in angular error of 5.2° for reciprocal target layouts and 7.2° for random target layouts when a user was 40% of the way through their movement. We also found that using just the current gaze location resulted in sufficient predictions in many conditions. We reflect on our results by discussing the broader implications of utilizing multimodal input to inform selection predictions in VR.

The rapid growth of AR platforms, combined with the rising predictive power of intelligent systems, will fundamentally change interactive computing. Interaction will increasingly happen on the go, causing I/O to become constrained, ultimately leading to reliance on user intent prediction for aid. In this pictorial, we argue that to support the development of such systems, new predictive UI toolkits are required. We place the reader in the shoes of an App designer and outline the challenges that will be faced. We then describe a new predictive toolkit, leveraging Fuzzy Widgets, or “Fidgets” as the main UI building block. Fidgets extend Responsive Design into the realm of intelligent systems, to adapt not only to spatial constraints, but to system predictions as well. We then describe a working implementation of a predictive music application, built using our described framework, showcasing its benefits and range of adaptive abilities.

Objects are indispensable tools in our daily lives. Recent research has demonstrated their potential to act as conduits for digital interactions with microgestures, however, the primary focus was on situations where the hand firmly grasps an object. We introduce GraspUI, an exploratory design space of object-centric gestures within the seven distinct phases of the grasping process, spanning pre-, during, and post-grasp movements. We conducted ideation sessions with mixed-reality designers from industry and academia to explore gesture integration throughout the entire grasping process. The outcome was 38 storyboards envisioning practical applications. To evaluate the design space’s utility, we performed a video-based assessment with end-users. We then implemented an interactive prototype and quantified the overhead cost of performing proposed gestures through a secondary study. Participants reacted positively to gestures and could integrate them into existing usage of objects. To conclude, we highlight technical and usability guidelines for implementing and extending GraspUI systems.

With the progress in Large Language Models (LLMs) and rapid development of wearable smart devices like smart glasses, there is a growing opportunity for users to interact with on-device virtual assistants through voice and gestures with ease. Although voice user interfaces (VUIs) have been widely studied, the potential uses of full-body gestures in VUIs that can fully understand users’ surroundings and gestures are relatively unexplored. In this two-phase research using a Wizard-of-Oz approach, we aim to investigate the role of gestures in VUI interactions and explore their design space. In an initial exploratory user study with six participants, we identify influential factors for VUI gestures and establish an initial design space. In the second phase, we conducted a user study with 12 participants to validate and refine our initial findings. Our results showed that users are open and ready to adopt and utilize gestures to interact with multi-modal VUIs, especially in scenarios with poor voice capture quality. The study also highlighted three key categories of gesture functions for enhancing multi-modal VUI interactions: context reference, alternative input, and flow control. Finally, we present a design space for multi-modal VUI gestures along with demonstrations to enlighten future design for coupling multi-modal VUIs with gestures.

Timely, personalized feedback is essential for students learning programming. LLM-powered tools like ChatGPT offer instant support, but reveal direct answers with code, which may hinder deep conceptual engagement. We developed CodeAid, an LLM-powered programming assistant delivering helpful, technically correct responses, without revealing code solutions. CodeAid answers conceptual questions, generates pseudo-code with line-by-line explanations, and annotates student’s incorrect code with fix suggestions. We deployed CodeAid in a programming class of 700 students for a 12-week semester. A thematic analysis of 8,000 usages of CodeAid was performed, further enriched by weekly surveys, and 22 student interviews. We then interviewed eight programming educators to gain further insights. Our findings reveal four design considerations for future educational AI assistants: D1) exploiting AI’s unique benefits; D2) simplifying query formulation while promoting cognitive engagement; D3) avoiding direct responses while encouraging motivated learning; and D4) maintaining transparency and control for students to asses and steer AI responses.

The progression to “Pervasive Augmented Reality” envisions easy access to multimodal information continuously. However, in many everyday scenarios, users are occupied physically, cognitively or socially. This may increase the friction to act upon the multimodal information that users encounter in the world. To reduce such friction, future interactive interfaces should intelligently provide quick access to digital actions based on users’ context. To explore the range of possible digital actions, we conducted a diary study that required participants to capture and share the media that they intended to perform actions on (e.g., images or audio), along with their desired actions and other contextual information. Using this data, we generated a holistic design space of digital follow-up actions that could be performed in response to different types of multimodal sensory inputs. We then designed OmniActions, a pipeline powered by large language models (LLMs) that processes multimodal sensory inputs and predicts follow-up actions on the target information grounded in the derived design space. Using the empirical data collected in the diary study, we performed quantitative evaluations on three variations of LLM techniques (intent classification, in-context learning and finetuning) and identified the most effective technique for our task. Additionally, as an instantiation of the pipeline, we developed an interactive prototype and reported preliminary user feedback about how people perceive and react to the action predictions and its errors.

Exploring alternative ideas by rewriting text is integral to the writing process. State-of-the-art Large Language Models (LLMs) can simplify writing variation generation. However, current interfaces pose challenges for simultaneous consideration of multiple variations: creating new variations without overwriting text can be difficult, and pasting them sequentially can clutter documents, increasing workload and disrupting writers’ flow. To tackle this, we present ABScribe, an interface that supports rapid, yet visually structured, exploration and organization of writing variations in human-AI co-writing tasks. With ABScribe, users can swiftly modify variations using LLM prompts, which are auto-converted into reusable buttons. Variations are stored adjacently within text fields for rapid in-place comparisons using mouse-over interactions on a popup toolbar. Our user study with 12 writers shows that ABScribe significantly reduces task workload (d = 1.20, p < 0.001), enhances user perceptions of the revision process (d = 2.41, p < 0.001) compared to a popular baseline workflow, and provides insights into how writers explore variations using LLMs.

Cross-reality tasks, like creating or consuming virtual reality (VR) content, often involve inconvenient or distracting switches between desktop and VR. An initial formative study explores cross-reality switching habits, finding most switches are momentary “peeks” between interfaces, with specific habits determined by current context. The results inform a design space for context-aware “peeking” techniques that allow users to view or interact with desktop from VR, and vice versa, without fully switching. We implemented a set of peeking techniques and evaluated them in two levels of a cross-reality task: one requiring only viewing, and another requiring input and viewing. Peeking techniques made task completion faster, with increased input accuracy and reduced perceived workload.

This work seeks to design and evaluate haptic feedback for sequential gestural inputs, where mid-air hand gestures are used to express system commands. Nine haptic patterns are first designed leveraging metaphors. To pursue efficient interaction, we examine the trade-off between pattern duration and recognition accuracy and find that durations as short as 0.3s-0.5s achieve roughly 80%-90% accuracy. We then examine the haptic design for sequential inputs, where we vary when the feedback for each gesture is provided, along with pattern duration, gesture sequence length, and age. Results show that providing haptic patterns right after detected hand gestures leads to significantly more efficient interaction compared with concatenating all haptic patterns after the gesture sequence. Moreover, the number of gestures had little impact on performance, but age is a significant predictor. Our results suggest that immediate feedback with 0.3s and 0.5s pattern duration would be recommended for younger and older users respectively.

When users wear a virtual reality (VR) headset, they lose access to their smartphone and accompanying apps. Past work has proposed smartphones as enhanced VR controllers, but little work has explored using existing smartphone apps and performing traditional smartphone interactions while in VR. In this paper, we consider three potential spatial anchorings for rendering smartphones in VR: On top of a tracked physical smartphone which the user holds (Phone-locked), on top of the user’s empty hand, as if holding a virtual smartphone (Hand-locked), or in a static position in front of the user (World-locked). We conducted a comparative study of target acquisition, swiping, and scrolling tasks across these anchorings using direct Touch or above-the-surface Pinch. Our findings indicate that physically holding a smartphone with Touch improves accuracy and speed for all tasks, and Pinch performed better with virtual smartphones. These findings provide a valuable foundation to enable smartphones in VR.

Input false-positive errors, where a system recognizes an input action that the user did not perform, have been shown to be particularly costly for user experience. Recent work has suggested that eye-gaze behavior immediately following an input event can be used to detect whether the input was intended by a user or was the result of a false-positive error. The ability to detect these errors could enable systems that assist the user with error recovery, but little is currently known about how such error mediation techniques might be designed, or the benefits they could provide. This paper presents an initial investigation of the design of error mediation techniques, and an evaluation of their potential benefits. A controlled study demonstrated that error mediation techniques can save time when recovering from errors by helping users to notice and resolve these errors quickly when they occur.

Synthesizers are powerful tools that allow musicians to create dynamic and original sounds. Existing commercial interfaces for synthesizers typically require musicians to interact with complex low-level parameters or to manage large libraries of premade sounds. To address these challenges, we implement SynthScribe — a fullstack system that uses multimodal deep learning to let users express their intentions at a much higher level. We implement features which address a number of difficulties, namely 1) searching through existing sounds, 2) creating completely new sounds, and 3) making meaningful modifications to a given sound. This is achieved with three main features: a multimodal search engine for a large library of synthesizer sounds; a user centered genetic algorithm by which completely new sounds can be created and selected given the users preferences; a sound editing support feature which highlights and gives examples for key control parameters with respect to a text or audio based query. The results of our user studies show SynthScribe is capable of reliably retrieving and modifying sounds while also affording the ability to create completely new sounds that expand a musicians creative horizon.

LLM-powered tools like ChatGPT Data Analysis, have the potential to help users tackle the challenging task of data analysis programming, which requires expertise in data processing, programming, and statistics. However, our formative study (n=15) uncovered serious challenges in verifying AI-generated results and steering the AI (i.e., guiding the AI system to produce the desired output). We developed two contrasting approaches to address these challenges. The first (Stepwise) decomposes the problem into step-by-step subgoals with pairs of editable assumptions and code until task completion, while the second (Phasewise) decomposes the entire problem into three editable, logical phases: structured input/output assumptions, execution plan, and code. A controlled, within-subjects experiment (n=18) compared these systems against a conversational baseline. Users reported significantly greater control with the Stepwise and Phasewise systems, and found intervention, correction, and verification easier, compared to the baseline. The results suggest design guidelines and trade-offs for AI-assisted data analysis tools.

Mixed Reality enables hybrid workspaces where physical and virtual monitors are adaptively created and moved to suit the current environment and needs. However, in shared settings, individual users’ workspaces are rarely aligned and can vary significantly in the number of monitors, available physical space, and workspace layout, creating inconsistencies between workspaces which may cause confusion and reduce collaboration. We present Desk2Desk, an optimization-based approach for remote collaboration in which the hybrid workspaces of two collaborators are fully integrated to enable immersive side-by-side collaboration. The optimization adjusts each user’s workspace in layout and number of shared monitors and creates a mapping between workspaces to handle inconsistencies between workspaces due to physical constraints (e.g. physical monitors). We show in a user study how our system adaptively merges dissimilar physical workspaces to enable immersive side-by-side collaboration, and demonstrate how an optimization-based approach can effectively address dissimilar physical layouts.

Although mid-air hand gestures have been widely adopted by VR/AR products (e.g., Quest 2 and HoloLens), some drawbacks remain due to their lack of tangibility and tactile feedback. Opportunistic Tangible User Interfaces could address these shortcomings by repurposing existing objects in one's physical environment. However, there has yet to be a systematic investigation of the gestures that would be desirable when using opportunistic objects or how such gestures would be impacted by such objects. In this work, we conducted an elicitation study to investigate the desirability of object and gesture combinations across a variety of interactions. The results contribute (1) an opportunistic tangible UI gesture set for spatial interfaces, and (2) an Affordance-Based Object Selector Scheme that identifies ideal objects for tangible input given a desired input gesture, based on that object's physical affordances. Arising from these findings is the vision of the Adaptive Tangible User Interface, which supports the on-the-fly composition of tangible interfaces based on the affordances found in the physical environment and a user's input task.

AI code generators like OpenAI Codex have the potential to assist novice programmers by generating code from natural language descriptions, however, over-reliance might negatively impact learning and retention. To explore the implications that AI code generators have on introductory programming, we conducted a controlled experiment with 69 novices (ages 10-17). Learners worked on 45 Python code-authoring tasks, for which half of the learners had access to Codex, each followed by a code-modification task. Our results show that using Codex significantly increased code-authoring performance (1.15x increased completion rate and 1.8x higher scores) while not decreasing performance on manual code-modification tasks. Additionally, learners with access to Codex during the training phase performed slightly better on the evaluation post-tests conducted one week later, although this difference did not reach statistical significance. Of interest, learners with higher Scratch pre-test scores performed significantly better on retention post-tests, if they had prior access to Codex.

Live and pre-recorded video tutorials are an effective means for teaching physical skills such as cooking or prototyping electronics. A dedicated cameraperson following an instructor’s activities can improve production quality. However, instructors who do not have access to a cameraperson’s help often have to work within the constraints of static cameras. We present Stargazer, a novel approach for assisting with tutorial content creation with a camera robot that autonomously tracks regions of interest based on instructor actions to capture dynamic shots. Instructors can adjust the camera behaviors of Stargazer with subtle cues, including gestures and speech, allowing them to fluidly integrate camera control commands into instructional activities. Our user study with six instructors, each teaching a distinct skill, showed that participants could create dynamic tutorial videos with a diverse range of subjects, camera framing, and camera angle combinations using Stargazer.

New immersive 3D design tools enable the creation of spatial design recordings, capturing collaborative design activities. By reviewing captured spatial design sessions, which include user activities, workflows, and tool use, users can reflect on their own design processes, learn new workflows, and understand others’ design rationale. However, finding interesting moments in design activities can be challenging: they contain multimodal data (such as user motion and logged events) occurring over time which can be difficult to specify when searching, and are typically distributed over many sessions or recordings. We present Tesseract, a Worlds-in-Miniature-based system to expressively query VR spatial design recordings. Tesseract consists of the Search Cube interface acting as a centralized stage-to-search container, and four querying tools for specifying multimodal data to enable users to find interesting moments in past design activities. We studied ten participants who used Tesseract and found support for our miniature-based stage-to-search approach.

Many XR devices use bare-hand gestures to reduce the need for handheld controllers. Such gestures, however, lead to false positive and false negative recognition errors, which detract from the user experience. While mediation techniques enable users to overcome recognition errors by clarifying their intentions via UI elements, little research has explored how mediation techniques should be designed in XR and how a user’s task and context may impact their design preferences. This research presents empirical studies about the impact of user perceived error costs on users’ preferences for three mediation technique designs, under different simulated scenarios that were inspired by real-life tasks. Based on a large-scale crowd-sourced survey and an immersive VR-based user study, our results suggest that the varying contexts within each task type can impact users’ perceived error costs, leading to different preferred mediation techniques. We further discuss the study implications of these results on future XR interaction design.

We present PinchLens, a new free-hand target selection technique for acquiring small and dense targets in Virtual Reality. Traditional pinch-based selection does not allow people to precisely manipulate small and dense objects effectively due to tracking and perceptual inaccuracies. Our approach combines spatial magnification, an adaptive control-display gain, and visual feedback to improve selection accuracy. When a user starts the pinching selection process, a magnifying bubble expands the scale of nearby targets, an adaptive control-to-display ratio is applied to the user’s hand for precision, and a cursor is displayed at the estimated pinch point for enhanced visual feedback. We performed a user study to compare our technique to traditional pinch selection and several variations to isolate the impact of each of the technique’s features. The results showed that PinchLens significantly outperformed traditional pinch selection, reducing error rates from 18.9% to 1.9%. Furthermore, we found that magnification was the dominant feature to produce this improvement, while the adaptive control-display gain and visual cursor of pinch were also helpful in several conditions.

Transitioning from block-based programming environments to text-based programming environments can be challenging as it requires students to learn new programming language concepts. In this paper, we identify and classify the issues encountered when transitioning from block-based to text-based programming. In particular, we investigate differences that emerge in learners when using a structured editor compared to an unstructured editor. We followed 26 high school students (ages 12-16; M=14 years) as they transitioned from Scratch to Python in three phases: (i) learning Scratch, (ii) transitioning from Scratch to Python using either a structured or unstructured editor, and (iii) evaluating Python coding skills using an unstructured editor. We identify 27 distinct types of issues and show that learners who used a structured editor during the transition phase had 4.6x less syntax issues and 1.9x less data-type issues compared to those who did not. When these learners switched to an unstructured editor for evaluation, they kept a lower rate on data-type issues but faced 4x more syntax errors.

Text-to-image generative models have demonstrated remarkable capabilities in generating high-quality images based on textual prompts. However, crafting prompts that accurately capture the user’s creative intent remains challenging. It often involves laborious trial-and-error procedures to ensure that the model interprets the prompts in alignment with the user’s intention. To address these challenges, we present Promptify, an interactive system that supports prompt exploration and refinement for text-to-image generative models. Promptify utilizes a suggestion engine powered by large language models to help users quickly explore and craft diverse prompts. Our interface allows users to organize the generated images flexibly, and based on their preferences, Promptify suggests potential changes to the original prompt. This feedback loop enables users to iteratively refine their prompts and enhance desired features while avoiding unwanted ones. Our user study shows that Promptify effectively facilitates the text-to-image workflow, allowing users to create visually appealing images on their first attempt while requiring significantly less cognitive load than a widely-used baseline tool.

While text entry is an essential and frequent task in Augmented Reality (AR) applications, devising an efficient and easy-to-use text entry method for AR remains an open challenge. This research presents STAR, a smartphone-analogous AR text entry technique that leverages a user’s familiarity with smartphone two-thumb typing. With STAR, a user performs thumb typing on a virtual QWERTY keyboard that is overlain on the skin of their hands. During an evaluation study of STAR, participants achieved a mean typing speed of 21.9 WPM (i.e., 56% of their smartphone typing speed), and a mean error rate of 0.3% after 30 minutes of practice. We further analyze the major factors implicated in the performance gap between STAR and smartphone typing, and discuss ways this gap could be narrowed.

In cinematic VR, viewers can only see a limited portion of the scene at any time. As a result, they may miss important events outside their field of view. While there are many techniques which offer spatial guidance (where to look), there has been little work on temporal guidance (when to look). Temporal guidance offers viewers a look-ahead time and allows viewers to plan their head motion for important events. This paper introduces spatiotemporal visual guidance and presents a new widget, RadarVR, which shows both spatial and temporal information of regions of interest (ROIs) in a video. Using RadarVR, we conducted a study to investigate the impact of temporal guidance and explore trade-offs between spatiotemporal and spatial-only visual guidance. Results show spatiotemporal feedback allows users to see a greater percentage of ROIs, with 81% more seen from their initial onset. We discuss design implications for future work in this space.

Knowledge workers frequently encounter repetitive web data entry tasks, like updating records or placing orders. Web automation increases productivity, but translating tasks to web actions accurately and extending to new specifications is challenging. Existing tools can automate tasks that perform the same logical trace of UI actions (e.g., input text in each field in order), but do not support tasks requiring different executions based on varied input conditions. We present DiLogics, a programming-by-demonstration system that utilizes NLP to assist users in creating web automation programs that handle diverse specifications. DiLogics first semantically segments input data to structured task steps. By recording user demonstrations for each step, DiLogics generalizes the web macros to novel but semantically similar task requirements. Our evaluation showed that non-experts can effectively use DiLogics to create automation programs that fulfill diverse input instructions. DiLogics provides an efficient, intuitive, and expressive method for developing web automation programs satisfying diverse specifications.

Virtual Reality (VR) can completely immerse users in a virtual world and provide little awareness of bystanders in the surrounding physical environment. Current technologies use predefined guardian area visualizations to set safety boundaries for VR interactions. However, bystanders cannot perceive these boundaries and may collide with VR users if they accidentally enter guardian areas. In this paper, we investigate four awareness techniques on mobile phones and smartwatches to help bystanders avoid invading guardian areas. These techniques include augmented reality boundary overlays and visual, auditory, and haptic alerts indicating bystanders' distance from guardians. Our findings suggest that the proposed techniques effectively keep participants clear of the safety boundaries. More specifically, using augmented reality overlays, participants could avoid guardians with less time, and haptic alerts caused less distraction.

Raycasting is a common method for target selection in virtual reality (VR). However, it results in selection ambiguity whenever a ray intersects multiple targets that are located at different depths. To resolve these ambiguities, we estimate object depth by projecting the closest intersection between the gaze and controller rays onto the controller ray. An evaluation of this method found that it significantly outperformed a previous eye convergence depth estimation technique. Based on these results, we developed GazeRayCursor, a novel selection technique that enhances Raycasting, by leveraging gaze for object depth estimation. In a second study, we compared two variations of GazeRayCursor with RayCursor, a recent technique developed for a similar purpose, in a dense target environment. The results indicated that GazeRayCursor decreased selection time by 45.0% and reduced manual depth adjustments by a factor of 10 in a dense target environment. Our findings showed that GazeRayCursor is an effective method for target disambiguation in VR selection without incurring extra effort.

In early stages of creative processes, practitioners externalize and combine inspirational materials, using strategies such as mood board creation to achieve a desired vision and aesthetic. Yet, collecting and combining materials can be difficult: (1) mood boards bias towards 2D images, neglecting audio, video, and 3D models; (2) alternative externalizations such as prototypes are best suited for later stages and can be time-consuming and tedious to create; and (3) online searches lead to disjointed sources between different websites and assets in the file system. To address these challenges, we created MoodCubes, a system for rapid creation and manipulation of multimedia content. When adding content, MoodCubes decomposes objects (e.g., extracting colour palettes), suggests new materials without the need to search (e.g., 3D models, images, lighting effects), and provides filters to change the scene’s aesthetic. We studied eight creative professionals using MoodCubes, which suggested ways the system might advance existing design practices.

Transitioning from block-based programming environments to conventional text-based programming languages is a challenge faced by many learners as they progress in their computer science education. In this paper, we introduce CodeStruct, a new intermediary programming environment for novices designed to support children who have prior experience with block-based programming to ease the eventual transition to text-based programming. We describe the development of CodeStruct and its key design features. We then present the results from a two-week long programming class with 26 high school students (ages 12-16; M=14 years) investigating how CodeStruct supported learners in transitioning from Scratch to Python. Our findings reveal how learners used the scaffolds designed into CodeStruct to support their transition from blocks to text, and that transitioning to CodeStruct reduced completion time (1.98x) and help requests (4.63x) when compared to transitioning directly to Python. Finally, learners that used CodeStruct, performed equally well (and slightly better in 10/16 programming activities) in their final transition to fully text-based Python programming.

How-to videos are often shot using camera angles that may not be optimal for learning motor tasks, with a prevalent use of third-person perspective. We present immersivePOV, an approach to film how-to videos from an immersive first-person perspective using a head-mounted 360° action camera. immersivePOV how-to videos can be viewed in a Virtual Reality headset, giving the viewer an eye-level viewpoint with three Degrees of Freedom. We evaluated our approach with two everyday motor tasks against a baseline first-person perspective and a third-person perspective. In a between-subjects study, participants were assigned to watch the task videos and then replicate the tasks. Results suggest that immersivePOV reduced perceived cognitive load and facilitated task learning. We discuss how immersivePOV can also streamline the video production process for content creators. Altogether, we conclude that immersivePOV is an effective approach to film how-to videos for learners and content creators alike.

Online synchronous tutoring allows for immediate engagement between instructors and audiences over distance. However, tutoring physical skills remains challenging because current telepresence approaches may not allow for adequate spatial awareness, viewpoint control of the demonstration activities scattered across an entire work area, and the instructor’s sufficient awareness of the audience. We present Asteroids, a novel approach for tangible robotic telepresence, to enable workbench-scale physical embodiments of remote people and tangible interactions by the instructor. With Asteroids, the audience can actively control a swarm of mini-telepresence robots, change camera positions, and switch to other robots’ viewpoints. Demonstrators can perceive the audiences’ physical presence while using tangible manipulations to control the audience’s viewpoints and presentation flow. We conducted an exploratory evaluation for Asteroids with 12 remote participants in a model-making tutorial scenario with an architectural expert demonstrator. Results suggest our unique features benefitted participants’ engagement, sense of presence, and understanding.

Touch interaction with physical smartphones and tablets in Virtual Reality offers interesting opportunities for cross-device input. Unfortunately, any imprecision in the alignment of the visual representation of either the hand or device can impact the precision of touch and the realism of the experience. We first study a user’s ability to rely solely on preoperative feedback to perform touch interaction in VR, where no rendering of the hand is provided. Results indicate that touch in VR is possible without a visual representation of the hand, but accuracy is influenced by how the device is held and the distance traveled to the target. We then introduce a dynamic calibration algorithm to minimize the offset between the physical hand and its virtual representation. In a second study, we show that this algorithm can increase touch accuracy by 43%, and minimize depth-based “screen penetration” or “floating touch” errors.

Remote teleoperation is an important robot control method when they cannot operate fully autonomously. Yet, teleoperation presents challenges to effective and full robot utilization: controls are cumbersome, inefficient, and the teleoperator needs to actively attend to the robot and its environment. Inspired by end-user programming, we propose a new interaction paradigm to support robot teleoperation for combinations of repetitive and complex movements. We introduce Mimic, a system that allows teleoperators to demonstrate and save robot trajectories as templates, and re-use them to execute the same action in new situations. Templates can be re-used through (1) macros—parametrized templates assigned to and activated by buttons on the controller, and (2) programs—sequences of parametrized templates that operate autonomously. A user study in a simulated environment showed that after initial set up time, participants completed manipulation tasks faster and more easily compared to traditional direct control.

Data scientists, researchers, and clerks often create web automation programs to perform repetitive yet essential tasks, such as data scraping and data entry. However, existing web automation systems lack mechanisms for defining conditional behaviors where the system can intelligently filter candidate content based on semantic filters (e.g., extract texts based on key ideas or images based on entity relationships). We introduce SemanticOn, a system that enables users to specify, refine, and incorporate visual and textual semantic conditions in web automation programs via two methods: natural language description via prompts or information highlighting. Users can coordinate with SemanticOn to refine the conditions as the program continuously executes or reclaim manual control to repair errors. In a user study, participants completed a series of conditional web automation tasks. They reported that SemanticOn helped them effectively express and refine their semantic intent by utilizing visual and textual conditions.

Gesture-based recognition systems are susceptible to input recognition errors and user errors, both of which negatively affect user experiences and can be frustrating to correct. Prior work has suggested that user gaze patterns following an input event could be used to detect input recognition errors and subsequently improve interaction. However, to be useful, error detection systems would need to detect various types of high-cost errors. Furthermore, to build a reliable detection model for errors, gaze behaviour following these errors must be manifested consistently across different tasks. Using data analysis and machine learning models, this research examined gaze dynamics following input events in virtual reality (VR). Across three distinct point-and-select tasks, we found differences in user gaze patterns following three input events: correctly recognized input actions, input recognition errors, and user errors. These differences were consistent across tasks, selection versus deselection actions, and naturally occurring versus experimentally injected input recognition errors. A multi-class deep neural network successfully discriminated between these three input events using only gaze dynamics, achieving an AUC-ROC-OVR score of 0.78. Together, these results demonstrate the utility of gaze in detecting interaction errors and have implications for the design of intelligent systems that can assist with adaptive error recovery.

Gaze-based interaction is a fast and ergonomic type of hands-free interaction that is often used with augmented and virtual reality when pointing at targets. Such interaction, however, can be cumbersome whenever user, tracking, or environmental factors cause eye tracking errors. Recent research has suggested that fallback modalities could be leveraged to ensure stable interaction irrespective of the current level of eye tracking error. This work thus presents Weighted Pointer interaction, a collection of error-aware pointing techniques that determine whether pointing should be performed by gaze, a fallback modality, or a combination of the two, depending on the level of eye tracking error that is present. These techniques enable users to accurately point at targets when eye tracking is accurate and inaccurate. A virtual reality target selection study demonstrated that Weighted Pointer techniques were more performant and preferred over techniques that required the use of manual modality switching.

Co-creative AI tools provide a method of creative collaboration between a user and machine. One form of co-creative AI called generative design requires the user to input design parameters and wait substantial periods of time while the system computes design solutions. We explore this interaction dynamic by providing an embodied experience in VR. Calliope is a virtual reality (VR) system that enables users to explore and manipulate generative design solutions in real time. Calliope accounts for the typical idle times in the generative design process by using a virtual environment to encourage parallelized and embodied data-exploration and synthesis, while maintaining a tight human-in-the-loop collaboration with the underlying algorithms. In this paper we discuss design considerations informed by formative studies with generative designers and artists and provide design guidelines to aid others in the development of co-creative AI systems in virtual environments.

This work explores the design of marking menus for gaze-based AR/VR menu selection by expert and novice users. It first identifies and explains the challenges inherent in ocular motor control and current eye tracking hardware, including overshooting, incorrect selections, and false activations. Through three empirical studies, we optimized and validated design parameters to mitigate these errors while reducing completion time, task load, and eye fatigue. Based on the findings from these studies, we derived a set of design guidelines to support gaze-based marking menus in AR/VR. To overcome the overshoot errors found with eye-based expert marking menu behaviour, we developed StickyPie, a marking menu technique that enables scale-independent marking input by estimating saccade landing positions. An evaluation of StickyPie revealed that StickyPie was easier to learn than the traditional technique (i.e., RegularPie) and was 10% more efficient after 3 sessions.

When users complete tasks on the computer, the knowledge they leverage and their intent is often lost because it is tedious or challenging to capture. This makes it harder to understand why a colleague designed a component a certain way or to remember requirements for software you wrote a year ago. We introduce think-aloud computing, a novel application of the think-aloud protocol where computer users are encouraged to speak while working to capture rich knowledge with relatively low effort. Through a formative study we find people shared information about design intent, work processes, problems encountered, to-do items, and other useful information. We developed a prototype that supports think-aloud computing by prompting users to speak and contextualizing speech with labels and application context. Our evaluation shows more subtle design decisions and process explanations were captured in think-aloud than via traditional documentation. Participants reported that think-aloud required similar effort as traditional documentation.

The craft of improvisational quilting involves working without the use of a predefined pattern. Design decisions are made “in the fabric,” with design experimentation tightly interleaved with the creation of the final artifact. To investigate how this type of design process can be supported, and to address challenges faced by practitioners, this paper presents PatchProv, a system for supporting improvisational quilt design. Based on a review of popular books on improvisational quilting, a set of design principles and key challenges to improvisational quilt design were identified, and PatchProv was developed to support the unique aspects of this process. An evaluation with a small group of quilters showed enthusiasm for the approach and revealed further possibilities for how computational tools can support improvisational quilting and improvisational design practices more broadly.

Learning musical instruments using online instructional videos has become increasingly prevalent. However, pre-recorded videos lack the instantaneous feedback and personal tailoring that human tutors provide. In addition, existing video navigations are not optimized for instrument learning, making the learning experience encumbered. Guided by our formative interviews with guitar players and prior literature, we designed Soloist, a mixed-initiative learning framework that automatically generates customizable curriculums from off-the-shelf guitar video lessons. Soloist takes raw videos as input and leverages deep-learning based audio processing to extract musical information. This back-end processing is used to provide an interactive visualization to support effective video navigation and real-time feedback on the user's performance, creating a guided learning experience. We demonstrate the capabilities and specific use-cases of Soloist within the domain of learning electric guitar solos using instructional YouTube videos. A remote user study, conducted to gather feedback from guitar players, shows encouraging results as the users unanimously preferred learning with Soloist over unconverted instructional videos.

Remotely controlled camera drones can support live, dynamic, and interactive virtual tours for travelers to overcome distance, expense, and health barriers. Yet, assigning one drone to one traveler may incur unnecessary waste of resources, and an abundance of concurrent drones raises safety concerns. While sharing the input and output of a single drone among multiple concurrent users can alleviate these limitations, standard control sharing protocols, such as turn-taking, are often inefficient. We present Constellation, a multi-user drone control system that synthesizes diverse user goals and generates efficient flight paths for the group. It supports point-of-interest specification on both static 3D environmental maps and live camera views. The generated paths minimize all users’ total extra waiting time. A web-based study with 16 participants show that Constellation could help groups navigate to their points-of-interest faster in comparison to the turn-taking baseline.

Interface designers often refer to UI behavior examples found in the wild (e.g., commercial websites) for reference or design inspiration. While past research has looked at retargeting interface and webpage design, limited work has explored the challenges in retargeting interactive visual behaviors. We introduce Umitation, a system that helps designers extract, edit, and adapt example front-end UI behaviors to target websites. Umitation can also help designers specify the desired behaviors and reconcile their intended interaction details with their existing UI. In a qualitative evaluation, we found evidence that Umitation helps participants extract and retarget dynamic front-end UI behavior examples quickly and expressively.

Existing approaches to trading off false positive versus false negative errors in input recognition are based on imprecise ideas of how these errors affect user experience that are unlikely to hold for all situations. To inform dynamic approaches to setting such a tradeoff, two user studies were conducted on how relative preference for false positive versus false negative errors is influenced by differences in the temporal cost of error recovery, and high-level task factors (time pressure, multi-tasking). Participants completed a tile selection task in which false positive and false negative errors were injected at a fixed rate, and the temporal cost to recover from each of the two types of error was varied, and then indicated a preference for one error type or the other, and a frustration rating for the task. Responses indicate that the temporal costs of error recovery can drive both frustration and relative error type preference, and that participants exhibit a bias against false positive errors, equivalent to ∼1.5 seconds or more of added temporal recovery time. Several explanations for this bias were revealed, including that false positive errors impose a greater attentional demand on the user, and that recovering from false positive errors imposes a task switching cost.

An increasingly popular way of experiencing remote places is by viewing 360° virtual tour videos, which show the surrounding view while traveling through an environment. However, finding particular locations in these videos can be difficult because current interfaces rely on distorted frame previews for navigation. To alleviate this usability issue, we propose Route Tapestries, continuous orthographic-perspective projection of scenes along camera routes. We first introduce an algorithm for automatically constructing Route Tapestries from a 360° video, inspired by the slit-scan photography technique. We then present a desktop video player interface using a Route Tapestry timeline for navigation. An online evaluation using a target-seeking task showed that Route Tapestries allowed users to locate targets 22% faster than with YouTube-style equirectangular previews and reduced the failure rate by 75% compared to a more conventional row-of-thumbnail strip preview. Our results highlight the value of reducing visual distortion and providing continuous visual contexts in previews for navigating 360°virtual tour videos.

Mobile User Interface Summarization generates succinct language descriptions of mobile screens for conveying important contents and functionalities of the screen, which can be useful for many language-based application scenarios. We present Screen2Words, a novel screen summarization approach that automatically encapsulates essential information of a UI screen into a coherent language phrase. Summarizing mobile screens requires a holistic understanding of the multi-modal data of mobile UIs, including text, image, structures as well as UI semantics, motivating our multi-modal learning approach. We collected and analyzed a large-scale screen summarization dataset annotated by human workers. Our dataset contains more than 112k language summarization across ∼ 22k unique UI screens. We then experimented with a set of deep models with different configurations. Our evaluation of these models with both automatic accuracy metrics and human rating shows that our approach can generate high-quality summaries for mobile screens. We demonstrate potential use cases of Screen2Words and open-source our dataset and model to lay the foundations for further bridging language and user interfaces.

This paper presents a new technique to predict the ray pointer landing position for selection movements in virtual reality (VR) environments. The technique adapts and extends a prior 2D kinematic template matching method to VR environments where ray pointers are used for selection. It builds on the insight that the kinematics of a controller and Head-Mounted Display (HMD) can be used to predict the ray's final landing position and angle. An initial study provides evidence that the motion of the head is a key input channel for improving prediction models. A second study validates this technique across a continuous range of distances, angles, and target sizes. On average, the technique's predictions were within 7.3° of the true landing position when 50% of the way through the movement and within 3.4° when 90%. Furthermore, compared to a direct extension of Kinematic Template Matching, which only uses controller movement, this head-coupled approach increases prediction accuracy by a factor of 1.8x when 40% of the way through the movement.

While synchronous one-on-one help for software learning is rich and valuable, it can be difficult to find and connect with someone who can provide assistance. Through a formative user study, we explore the idea of fixed-duration, one-on-one help sessions and find that 3 minutes is often enough time for novice users to explain their problem and receive meaningful help from an expert. To facilitate this type of interaction, we developed MicroMentor, an on-demand help system that connects users via video chat for 3-minute help sessions. MicroMentor automatically attaches relevant supplementary materials and uses contextual information, such as command history and expertise, to encourage the most qualified users to accept incoming requests. These help sessions are recorded and archived, building a bank of knowledge that can further help a broader audience. Through a user study, we find MicroMentor to be useful and successful in connecting users for short teaching moments.

Input techniques have been drawing abiding attention along with the continual miniaturization of personal computers. In this paper, we present BlyncSync, a novel multi-modal gesture set that leverages the synchronicity of touch and blink events to augment the input vocabulary of smartwatches with a rapid gesture, while at the same time, offers a solution to the false activation problem of blink-based input. BlyncSync contributes the concept of a mutual delimiter, where two modalities are used to jointly delimit the intention of each other's input. A study shows that BlyncSync is 33% faster than using a baseline input delimiter (physical smartwatch button), with only 150ms in overhead cost compared to traditional touch events. Furthermore, our data indicates that the gesture can be tuned to elicit a true positive rate of 97% and a false positive rate of 1.68%.

In pursuit of a future where HMD devices can be used in tandem with smartphones and other smart devices, we present BISHARE, a design space of cross-device interactions between smartphones and ARHMDs. Our design space is unique in that it is bidirectional in nature, as it examines how both the HMD can be used to enhance smartphone tasks, and how the smartphone can be used to enhance HMD tasks. We then present an interactive prototype that enables cross-device interactions across the proposed design space. A 12-participant user study demonstrates the promise of the design space and provides insights, observations, and guidance for the future.

This paper introduces Workflow graphs, or W-graphs, which encode how the approaches taken by multiple users performing a fixed 3D design task converge and diverge from one another. The graph's nodes represent equivalent intermediate task states across users, and directed edges represent how a user moved between these states, inferred from screen recording videos, command log data, and task content history. The result is a data structure that captures alternative methods for performing sub-tasks (e.g., modeling the legs of a chair) and alternative strategies of the overall task. As a case study, we describe and exemplify a computational pipeline for building W-graphs using screen recordings, command logs, and 3D model snapshots from an instrumented version of the Tinkercad 3D modeling application, and present graphs built for two sample tasks. We also illustrate how W-graphs can facilitate novel user interfaces with scenarios in workflow feedback, on-demand task guidance, and instructor dashboards.

Camera drones, a rapidly emerging technology, offer people the ability to remotely inspect an environment with a high degree of mobility and agility. However, manual remote piloting of a drone is prone to errors. In contrast, autopilot systems can require a significant degree of environmental knowledge and are not necessarily designed to support flexible visual inspections. Inspired by camera manipulation techniques in interactive graphics, we designed StarHopper, a novel touch screen interface for efficient object-centric camera drone navigation, in which a user directly specifies the navigation of a drone camera relative to a specified object of interest. The system relies on minimal environmental information and combines both manual and automated control mechanisms to give users the freedom to remotely explore an environment with efficiency and accuracy. A lab study shows that StarHopper offers an efficiency gain of 35.4% over manual piloting, complimented by an overall user preference towards our object-centric navigation system.

Augmented Reality (AR) can assist with physical tasks such as object assembly through the use of situated instructions. These instructions can be in the form of videos, pictures, text or guiding animations, where the most helpful media among these is highly dependent on both the user and the nature of the task. Our work supports the authoring of AR tutorials for assembly tasks with little overhead beyond simply performing the task itself. The presented system, AuthAR reduces the time and effort required to build interactive AR tutorials by automatically generating key components of the AR tutorial while the author is assembling the physical pieces. Further, the system guides authors through the process of adding videos, pictures, text and animations to the tutorial. This concurrent assembly and tutorial generation approach allows for authoring of portable tutorials that fit the preferences of different end users.

Training a state-of-the-art deep neural network (DNNs) is a computationally-expensive and time-consuming process, which incentivizes deep learning developers to debug their DNNs for computational performance. However, effectively performing this debugging requires intimate knowledge about the underlying software and hardware systems-something that the typical deep learning developer may not have. To help bridge this gap, we present Skyline: a new interactive tool for DNN training that supports in-editor computational performance profiling, visualization, and debugging. Skyline's key contribution is that it leverages special computational properties of DNN training to provide (i) interactive performance predictions and visualizations, and (ii) directly manipulatable visualizations that, when dragged, mutate the batch size in the code. As an in-editor tool, Skyline allows users to leverage these diagnostic features to debug the performance of their DNNs during development. An exploratory qualitative user study of Skyline produced promising results; all the participants found Skyline to be useful and easy to use.

Design professionals increasingly need to learn and use multiple feature-rich software to design complex artifacts in a variety of domains. In this work, we aimed to understand how design professionals learn new software features and seek help in today's modern workplace of interconnected spaces and increased access to shared online learning resources. We conducted 20 interviews with design professionals working in different disciplines such as architecture and planning, construction, structural design, and broadcast media, who used complex feature-rich applications on a daily basis. We found that despite the wide availability of learning resources on the web and decades of research dedicated to workplace knowledge management tools, design professionals still rely heavily on interpersonal help-seeking. Furthermore, while companies have devised a variety of technological channels to answer employees' help requests, such intermediate tools were often abandoned, and help-seeking conversations shifted to face-to-face learning episodes. Our findings point to design opportunities and shortcomings in the design of remote communication tools when seeking help for feature-rich software applications.

Past work in augmented reality has shown that temperature-associated AR stimuli can induce warming and cooling sensations in the user, and prior work in psychology suggests that a person’s body temperature can influence that person’s sense of subjective perception of duration. In this paper, we present a user study to evaluate the relationship between temperature-associated virtual stimuli presented on an AR-HMD and the user’s sense of subjective perception of duration and temperature. In particular, we investigate two independent variables: the apparent temperature of the virtual stimuli presented to the participant, which could be hot or cold, and the location of the stimuli, which could be in direct contact with the user, in indirect contact with the user, or both in direct and indirect contact simultaneously. We investigate how these variables affect the users’ perception of duration and perception of body and environment temperature by having participants make prospective time estimations while observing the virtual stimulus and answering subjective questions regarding their body and environment temperatures. Our work confirms that temperature-associated virtual stimuli are capable of having significant effects on the users’ perception of temperature, and highlights a possible limitation in the current augmented reality technology in that no secondary effects on the users’ perception of duration were observed.

Expressive robots are useful in many contexts, from industrial to entertainment applications. However, designing expressive robot behaviors requires editing a large number of unintuitive control parameters. We present an interactive, data-driven system that allows editing of these complex parameters in a semantic space. Our system combines a physics-based simulation that captures the robot's motion capabilities, and a crowd-powered framework that extracts relationships between the robot's motion parameters and the desired semantic behavior. These relationships enable mixed-initiative exploration of possible robot motions. We specifically demonstrate our system in the context of designing emotionally expressive behaviors. A user-study finds the system to be useful for more quickly developing desirable robot behaviors, compared to manual parameter editing.

The recent proliferation of fabrication and making activities has introduced a large number of users to a variety of tools and equipment. Monitored, reactive and adaptive fabrication spaces are needed to provide personalized information, feedback and assistance to users. This paper explores the sensorization of making and fabrication activities, where the environment, tools, and users were considered to be separate entities that could be instrumented for data collection. From this exploration, we present the design of a modular system that can capture data from the varied sensors and infer contextual information. Using this system, we collected data from fourteen participants with varying levels of expertise as they performed seven representative making tasks. From the collected data, we predict which activities are being performed, which users are performing the activities, and what expertise the users have. We present several use cases of this contextual information for future interactive fabrication spaces.

Remotely instructing and guiding users in physical tasks has offered promise across a wide variety of domains. While it has been the subject of many research projects, current approaches are often limited in the communication bandwidth (lacking context, spatial information) or interactivity (unidirectional, asynchronous) between the expert and the learner. Systems that use Mixed-Reality systems for this purpose have rigid configurations for the expert and the learner. We explore the design space of bi-directional mixed-reality telepresence systems for teaching physical tasks, and present Loki, a novel system which explores the various dimensions of this space. Loki leverages video, audio and spatial capture along with mixed-reality presentation methods to allow users to explore and annotate the local and remote environments, and record and review their own performance as well as their peer's. The system design of Loki also enables easy transitions between different configurations within the explored design space. We validate its utility through a varied set of scenarios and a qualitative user study.

Growing interest in personal fabrication has resulted in many ways to ideate, design, and prototype, in addition to studies of who a maker is and the challenges they face. Less attention, however, has focused on the role of the environment in fabrication processes. By understanding how interactions with tools, fixtures, materials, and spaces shape workflows, we can better determine how to design the next generation of workshops, design tools, and fabrication equipment to support personal fabrication activities. To build this understanding, site visits and interviews at local makerspaces, fabrication studios, and workshops were conducted. These visits uncovered the rich practices and roadblocks generated by workshops today. The observations identified the importance of spatial layouts, territoriality and occupant agency, distributed knowledge, and organizational flux, among others, to design and fabrication processes. These observations were further synthesized into one possible direction for such spaces: hybrid workshops (i.e., environments that can leverage computation and responsive architecture to enhance a maker's ability to design and fabricate). This work identifies how such spaces could harness the rich practices and eliminate the challenges found with workshops today and discusses the technical innovations and philosophical questions that hybrid workshops will pose to the future of personal fabrication.

We present SymbiosisSketch, a hybrid sketching system that combines drawing in air (3D) and on a drawing surface (2D) to create detailed 3D designs of arbitrary scale in an augmented reality (AR) setting. SymbiosisSketch leverages the complementary affordances of 3D (immersive, unconstrained, life-sized) and 2D (precise, constrained, ergonomic) interactions for in situ 3D conceptual design. A defining aspect of our system is the ongoing creation of surfaces from unorganized collections of 3D curves. These surfaces serve a dual purpose: as 3D canvases to map strokes drawn on a 2D tablet, and as shape proxies to occlude the physical environment and hidden curves in a 3D sketch. SymbiosisSketch users draw interchangeably on a 2D tablet or in 3D within an ergonomically comfortable canonical volume, mapped to arbitrary scale in AR. Our evaluation study shows this hybrid technique to be easy to use in situ and effective in transcending the creative potential of either traditional sketching or drawing in air.

Low-cost fabrication machines (e.g., 3D printers) offer the promise of creating custom-designed objects by a range of users. To maximize performance, generative design methods such as topology optimization can automatically optimize properties of a design based on high-level specifications. Though promising, such methods require people to map their design ideas--often unintuitively--to a small number of mathematical input parameters, and the relationship between those parameters and a generated design is often unclear, making it difficult to iterate a design. We present Forte, a sketch-based, real-time interactive tool for people to directly express and iterate on their designs via 2D topology optimization. Users can ask the system to add structures, provide a variation with better performance, or optimize internal material layouts. Users can globally control how much to 'deviate' from the initial sketch, or perform local suggestive editing, which interactively prompts the system to update based on the new information. Design sessions with 10 participants demonstrate that Forte empowers designers to create and explore a range of optimized designs with custom forms and styles.

3D design software is increasingly available to children through libraries, maker spaces, and for free on the web. This unprecedented availability has the potential to unleash children's creativity in cutting edge domains, but is limited by the steep learning curve of the software. Unfortunately, there is little past work studying the breakdowns faced by children in this domain-most past work has focused on adults in professional settings. In this paper, we present a study of online learning resources and help-seeking strategies available to children starting out with 3D design software. We find that children face a range of challenges when trying to learn 3D design independently-tutorials present instructions at a granularity that leads to overlooked and incorrectly-performed actions, and online help-seeking is largely ineffective due to challenges with query formulation and evaluating found information. Based on our findings, we recommend design directions for next-generation help and learning systems tailored to children.

This paper presents Dream Lens, an interactive visual analysis tool for exploring and visualizing large-scale generative design datasets. Unlike traditional computer aided design, where users create a single model, with generative design, users specify high-level goals and constraints, and the system automatically generates hundreds or thousands of candidates all meeting the design criteria. Once a large collection of design variations is created, the designer is left with the task of finding the design, or set of designs, which best meets their requirements. This is a complicated task which could require analyzing the structural characteristics and visual aesthetics of the designs. Two studies are conducted which demonstrate the usability and usefulness of the Dream Lens system, and a generatively designed dataset of 16,800 designs for a sample design problem is described and publicly released to encourage advancement in this area.

Users of complex software applications often rely on inefficient or suboptimal workflows because they are not aware that better methods exist. In this paper, we develop and validate a hierarchical approach combining topic modeling and frequent pattern mining to classify the workflows offered by an application, based on a corpus of community-generated videos and command logs. We then propose and evaluate a design space of four different workflow recommender algorithms, which can be used to recommend new workflows and their associated videos to software users. An expert validation of the task classification approach found that 82% of the time, experts agreed with the classifications. We also evaluate our workflow recommender algorithms, demonstrating their potential and suggesting avenues for future work.

Instructors of 3D design workshops for children face many challenges, including maintaining awareness of students' progress, helping students who need additional attention, and creating a fun experience while still achieving learning goals. To help address these challenges, we developed Maestro, a workshop orchestration system that visualizes students' progress, automatically detects and draws attention to common challenges faced by students, and provides mechanisms to address common student challenges as they occur. We present the design of Maestro, and the results of a case-study evaluation with an experienced facilitator and 13 children. The facilitator appreciated Maestro's real-time indications of which students were successfully following her tutorial demonstration, and recognized the system's potential to "extend her reach" while helping struggling students. Participant interaction data from the study provided support for our follow-along detection algorithm, and the capability to remind students to use 3D navigation.

Current haptic devices are often bulky and rigid, making them unsuitable for ubiquitous interaction and scenarios where the user must also interact with the real world. To address this gap, we propose HydroRing, an unobtrusive, finger-worn device that can provide the tactile sensations of pressure, vibration, and temperature on the fingertip, enabling mixed-reality haptic interactions. Different from previous explorations, HydroRing in active mode delivers sensations using liquid travelling through a thin, flexible latex tube worn across the fingerpad, and has minimal impact on a user's dexterity and their perception of stimuli in passive mode. Two studies evaluated participants' ability to perceive and recognize sensations generated by the device, as well as their ability to perceive physical stimuli while wearing the device. We conclude by exploring several applications leveraging this mixed-reality haptics approach.

Learning a new software application can be a challenge, requiring the user to enter a new environment where their existing knowledge and skills do not apply, or worse, work against them. To ease this transition, we propose the idea of cross-application bridges that start with the interface of a familiar application, and gradually change their interaction model, tools, conventions, and appearance to resemble that of an application to be learned. To investigate this idea, we developed Blocks-to-CAD, a cross-application bridge from Minecraft-style games to 3D solid modeling. A user study of our system demonstrated that our modifications to the game did not hurt enjoyment or increase cognitive load, and that players could successfully apply knowledge and skills learned in the game to tasks in a popular 3D solid modeling application. The process of developing Blocks-to-CAD also revealed eight design strategies that can be applied to design cross-application bridges for other applications and domains.

A wide variety of tools for creating physical computing systems have been developed, but getting started in this domain remains challenging for novices. In this paper, we introduce test-driven physical computing tutorials, a novel application of interactive tutorial systems to better support users in building and programming physical computing systems. These tutorials inject interactive tests into the tutorial process to help users verify and understand individual steps before proceeding. We begin by presenting a taxonomy of the types of tests that can be incorporated into physical computing tutorials. We then present ElectroTutor, a tutorial system that implements a range of tests for both the software and physical aspects of a physical computing system. A user study suggests that ElectroTutor can improve users' success and confidence when completing a tutorial, and save them time by reducing the need to backtrack and troubleshoot errors made on previous tutorial steps.

We present an interactive tool to animate the visual elements of a static picture, based on simple sketch-based markup. While animated images enhance websites, infographics, logos, e-books, and social media, creating such animations from still pictures is difficult for novices and tedious for experts. Creating automatic tools is challenging due to ambiguities in object segmentation, relative depth ordering, and non-existent temporal information. With a few user drawn scribbles as input, our mixed initiative creative interface extracts repetitive texture elements in an image, and supports animating them. Our system also facilitates the creation of multiple layers to enhance depth cues in the animation. Finally, after analyzing the artwork during segmentation, several animation processes automatically generate kinetic textures that are spatio-temporally coherent with the source image. Our results, as well as feedback from our user evaluation, suggest that our system effectively allows illustrators and animators to add life to still images in a broad range of visual styles.

Sketching in immersive 3D virtual reality (VR) environments has great potential for a variety of interactive 3D design applications. Precisely sketching the intended strokes in mid-air, however, can be a challenge. In this paper, we present a set of controlled studies to analyze the factors affecting human ability to sketch freely in a 3D VR environment. In our first study, we directly compare traditional sketching on a physical surface to sketching in VR, with and without a physical surface to rest the stylus on. Our results indicate that the lack of a physical drawing surface is a major cause of inaccuracies in VR drawing, and that the effect is dependent on the orientation of the drawing surface. In a second experiment, we evaluate the extent to which visual guidance can compensate for the loss of sketching precision in VR. We found that while additional visual guidance improves positional accuracy, it can be detrimental to the aesthetic quality of strokes. We conclude by distilling our experimental findings into design guidelines for sketching tools in immersive 3D environments.

Physical construction and assembly tasks are often carried out by groups of collocated workers, and they can be difficult to coordinate. Group members must spend time deciding how to split up the task, how to assign subtasks to each other, and in what order subtasks should be completed. Informed by an observational study examining group coordination challenges, we built a task distribution system called WeBuild. Our custom algorithm dynamically assigns subtasks to workers in a group, taking into account factors such as the dependencies between subtasks and the skills of each group member. Each worker views personalized step-by-step instructions on a mobile phone, while a dashboard visualizes the entire process. An initial study found that WeBuild reduced the start-up time needed to coordinate and begin a task, and provides direction for future research to build on toward improving group efficiency and coordination for complex tasks.

Interactive, smart objects-customized to individuals and uses-are central to many movements, such as tangibles, the internet of things (IoT), and ubiquitous computing. Yet, rapid prototyping both the form and function of these custom objects can be problematic, particularly for those with limited electronics or programming experience. Designers often need to embed custom circuitry; program its workings; and create a form factor that not only reflects the desired user experience but can also house the required circuitry and electronics. To mitigate this, we created Pineal, a design tool that lets end-users: (1) modify 3D models to include a smart watch or phone as its heart; (2) specify high-level interactive behaviours through visual programming; and (3) have the phone or watch act out such behaviours as the objects' "smarts". Furthermore, a series of prototypes show how Pineal exploits mobile sensing and output, and automatically generates 3D printed form-factors for rich, interactive, objects.

The dramatic decrease in price and increase in availability of hobbyist electronics has led to a wide array of embedded and interactive devices. While electronics have become more widespread, developing and prototyping the required circuitry for these devices is still difficult, requiring knowledge of electronics, components, and programming. In this paper, we present Trigger-Action-Circuits (TAC), an interactive system that leverages generative design to produce circuitry, firmware, and assembly instructions, based on high-level, behavioural descriptions. TAC is able to generate multiple candidate circuits from a behavioural description, giving the user a number of alternative circuits that may be best suited to their use case (e.g., based on cost, component availability or ease of assembly). The generated circuitry uses off-the-shelf, commodity electronics, not specialized hardware components, enabling scalability and extensibility. TAC supports a range of common components and behaviors that are frequently required for prototyping electronic circuits. A user study demonstrated that TAC helps users avoid problems encountered during circuit design and assembly, with users completing their circuits significantly faster than with traditional methods.

We present DreamSketch, a novel 3D design interface that combines the free-form and expressive qualities of sketching with the computational power of generative design algorithms. In DreamSketch, a user coarsely defines the problem by sketching the design context. Then, a generative design algorithm produces multiple solutions that are augmented as 3D objects in the sketched context. The user can interact with the scene to navigate through the generated solutions. The combination of sketching and generative algorithms enables designers to explore multiple ideas and make better informed design decisions during the early stages of design. Design study sessions with designers and mechanical engineers demonstrate the expressive nature and creative possibilities of DreamSketch.

Within this work, a novel metaphor, haunted design, is explored to challenge the definitions of "display" used today. Haunted design draws inspiration and vision from some of the most multi-modal and sensory diverse experiences that have been reported, the paranormal and hauntings. By synthesizing and deconstructing such phenomena, four novel opportunities to direct display design were uncovered, e.g., intensity, familiarly, tangibility, and shareability. A large-scale design probe, The Living Room, guided the ideation and prototyping of design concepts that exemplify facets of haunted design. By combining the opportunities, design concepts, and survey responses, a framework highlighting the importance of objects, their behavior, and the resulting phenomena to haunted design was developed. Given its emphasis on the odd and unusual, the haunted design metaphor should great spur conversation and alternative directions for future display-based user experiences.

There is a long tradition for crafting wearable objects directly on the body, such as garments, casts, and orthotics. However, these high-skill, analog practices have yet to be augmented by digital fabrication techniques. In this paper, we explore the use of hybrid fabrication workflows for on-body printing. We outline design considerations for creating on-body fabrication systems, and identify several human, machine, and material challenges unique to this endeavor. Based on our explorations, we present ExoSkin, a hybrid fabrication system for designing and printing digital artifacts directly on the body. ExoSkin utilizes a custom built fabrication machine designed specifically for on-body printing. We demonstrate the potential of on-body fabrication with a set of sample workflows, and share feedback from initial observation sessions.

We present ChronoFab, a 3D modeling tool to craft motion sculptures, tangible representations of 3D animated models, visualizing an object's motion with static, transient, ephemeral visuals that are left behind. Our tool casts 3D modeling as a dynamic art-form by employing 3D animation and dynamic simulation for the modeling of motion sculptures. Our work is inspired by the rich history of stylized motion depiction techniques in existing 3D motion sculptures and 2D comic art. Based on a survey of such techniques, we present an interface that enables users to rapidly explore and craft a variety of static 3D motion depiction techniques, including motion lines, multiple stroboscopic stamps, sweeps and particle systems, using a 3D animated object as input. In a set of professional and non-professional usage sessions, ChronoFab was found to be a superior tool for the authoring of motion sculptures, compared to traditional 3D modeling workflows, reducing task completion times by 79%.

We present a sketching tool for crafting animated illustrations that contain the exaggerated dynamics of stylized 2D animations. The system provides a set of motion amplifiers which implement a set of established principles of 2D animation. These amplifiers break down a complex animation effect into independent, understandable chunks. Each amplifier imposes deformations to an underlying grid, which in turn updates the corresponding strokes. Users can combine these amplifiers at will when applying them to an existing animation, promoting rapid experimentation. By leveraging the freeform nature of sketching, our system allows users to rapidly sketch, record motion, explore exaggerated dynamics using the amplifiers, and fine-tune their animations. Practical results confirm that users with no prior experience in animation can produce expressive animated illustrations quickly and easily.

Small touchscreens worn on the wrist are becoming increasingly common, but standard interaction techniques for these devices can be slow, requiring a series of coarse swipes and taps to perform an action. To support faster command selection on watches, we investigate two related interaction techniques that exploit spatial memory. WristTap uses multitouch to allow selection in a single action, and TwoTap uses a rapid combination of two sequential taps. In three quantitative studies, we investigate the design and performance of these techniques in comparison to standard methods. Results indicate that both techniques are feasible, able to accommodate large numbers of commands, and fast users are able to quickly learn the techniques and reach performance of ~1.0 seconds per selection, which is approximately one-third of the time of standard commercial techniques. We also provide insights into the types of applications for which these techniques are well-suited, and discuss how the techniques could be extended.

Sliders and Visual Analogue Scales (VASs) are input mechanisms which allow users to specify a value within a predefined range. At a minimum, sliders and VASs typically consist of a line with the extreme values labeled. Additional decorations such as labels and tick marks can be added to give information about the gradations along the scale and allow for more precise and repeatable selections. There is a rich history of research about the effect of labelling in discrete scales (i.e., Likert scales), however the effect of decorations on continuous scales has not been rigorously explored. In this paper we perform a 2,000 user, 250,000 trial online experiment to study the effects of slider appearance, and find that decorations along the slider considerably bias the distribution of responses received. Using two separate experimental tasks, the trade-offs between bias, accuracy, and speed-of-use are explored and design recommendations for optimal slider implementations are proposed.

We present RetroFab, an end-to-end design and fabrication environment that allows non-experts to retrofit physical interfaces. Our approach allows for changing the layout and behavior of physical interfaces. Unlike customizing software interfaces, physical interfaces are often challenging to adapt because of their rigidity. With RetroFab, a new physical interface is designed that serves as a proxy interface for the legacy controls that are now operated by actuators. RetroFab makes this concept of retrofitting devices available to non-experts by automatically generating an enclosure structure from an annotated 3D scan. This enclosure structure holds together actuators, sensors as well as components for the redesigned interface. To allow retrofitting a wide variety of legacy devices, the RetroFab design tool comes with a toolkit of 12 components. We demonstrate the versatility and novel opportunities of our approach by retrofitting five domestic objects and exploring their use cases. Preliminary user feedback reports on the experience of retrofitting devices with RetroFab.

We present Object-Oriented Drawing, which replaces most WIMP UI with Attribute Objects. Attribute Objects embody the attributes of digital content as UI objects that can be manipulated through direct touch gestures. In the paper, the fundamental UI concepts are presented, including Attribute Objects, which may be moved, cloned, linked, and freely associated with drawing objects. Other functionalities, such as attribute-level blending and undo, are also demonstrated. We developed a drawing application based on the presented concepts with simultaneous touch and pen input. An expert assessment of our application shows that direct physical manipulation of Attribute Objects enables a user to quickly perform interactions which were previously tedious, or even impossible, with a coherent and consistent interaction experience throughout the entire interface.

Everyday tools and objects often need to be customized for an unplanned use or adapted for specific user, such as adding a bigger pull to a zipper or a larger grip for a pen. The advent of low-cost 3D printing offers the possibility to rapidly construct a wide range of such adaptations. However, while 3D printers are now affordable enough for even home use, the tools needed to design custom adaptations normally require skills that are beyond users with limited 3D modeling experience.

In recent years, extensive research in the HCI literature has explored interactive techniques for digital fabrication. However, little attention in this body of work has examined how to involve and guide human workers in fabricating larger-scale structures. We propose a novel model of crowdsourced fabrication, in which a large number of workers and volunteers are guided through the process of building a pre-designed structure. The process is facilitated by an intelligent construction space capable of guiding individual workers and coordinating the overall build process. More specifically, we explore the use of smartwatches, indoor location sensing, and instrumented construction materials to provide real-time guidance to workers, coordinated by a foreman engine that manages the overall build process. We report on a three day deployment of our system to construct a 12-tall bamboo pavilion with assistance from more than one hundred volunteer workers, and reflect on observations and feedback collected during the exhibit.

Dynamic effects such as waves, splashes, fire, smoke, and explosions are an integral part of stylized animations. However, such dynamics are challenging to produce, as manually sketching key-frames requires significant effort and artistic expertise while physical simulation tools lack sufficient expressiveness and user control. We present an interactive interface for designing these elemental dynamics for animated illustrations. Users draw with coarse-scale energy brushes which serve as control gestures to drive detailed flow particles which represent local velocity fields. These fields can convey both realistic and artistic effects based on user specification. This painting metaphor for creating elemental dynamics simplifies the process, providing artistic control, and preserves the fluidity of sketching. Our system is fast, stable, and intuitive. An initial user evaluation shows that even novice users with no prior animation experience can create intriguing dynamics using our system.

Mobile devices offer constant connectivity to the world, which can negatively affect in-person interaction. Current approaches to minimizing the social disruption and improving the subtlety of interactions tend to focus on the development of inconspicuous devices that provide basic input or output. This paper presents a more general approach to subtle interaction and demonstrates how a number of principles from magic can be leveraged to improve subtlety. It also presents a framework that can be used to classify subtle interfaces along with a modular set of novel interfaces that fit within this framework. Lastly, the paper presents a new evaluation paradigm specifically designed to assess the subtlety of interactions. This paradigm is used to compare traditional approaches to our new subtle approaches. We find our new approaches are over five times more subtle than traditional interactions, even when participants are aware of the technologies being used.

Skin-based input has become an increasingly viable interaction model for user interfaces, however it has yet to be explored outside the domain of mobile computing. In this paper, we examine skin as an interactive input surface for gestural 3D modeling-to-fabrication systems. When used as both the input surface and base canvas for digital design, skin-input can enable non-experts users to intuitively create precise forms around highly complex physical contexts: our own bodies. In this paper, we outline design considerations when creating interfaces for such systems. We then discuss interaction techniques for three different modes of skin-centric modeling: direct, parametric, and generative. We also present Tactum, a new fabrication-aware design system that captures a user's skin-centric gestures for 3D modeling directly on the body. Lastly, we show sample artifacts generated with our system, and share a set of observations from design professionals.

Text entry for smart eyewear is generally limited to speech-based input due to constraints of the input channels. However, many smart eyewear devices are now including a side touchpad making gesture-based text entry feasible. The Swipeboard technique, recently proposed for ultra-small touch screens such as smart watches, may be particularly suitable for smart eyewear: unlike other recent text-entry techniques for small devices, it supports eyes-free input. We investigate the limitations and feasibility of implementing Swipeboard on smart eyewear, using the side touch pad for input. Our first study reveals usability and recognition problems of using the side touch pad to perform the required gestures. To address these problems, we propose SwipeZone, which replaces diagonal gestures with zone-specific swipes. In a text entry study, we show that our redesign achieved a WPM rate of 8.73, 15.2% higher than Swipeboard, with a statistically significant improvement in the last half of the study blocks.

In this work, we explore moveables, i.e., interactive papercraft that harness user interaction to generate visual effects. First, we present a survey of children's books that captured the state of the art of moveables. The results of this survey were synthesized into a moveable taxonomy and informed MoveableMaker, a new tool to assist users in designing, generating, and assembling moveable papercraft. MoveableMaker supports the creation and customization of a number of moveable effects and employs moveable-specific features including animated tooltips, automatic instruction generation, constraint-based rendering, techniques to reduce material waste, and so on. To understand how MoveableMaker encourages creativity and enhances the workflow when creating moveables, a series of exploratory workshops were conducted. The results of these explorations, including the content participants created and their impressions, are discussed, along with avenues for future research involving moveables.

The growth of mobile and wearable technologies has made it often difficult to understand what people in our surroundings are doing with their technology. In this paper, we introduce the concept of candid interaction: techniques for providing awareness about our mobile and wearable device usage to others in the vicinity. We motivate and ground this exploration through a survey on current attitudes toward device usage during interpersonal encounters. We then explore a design space for candid interaction through seven prototypes that leverage a wide range of technological enhancements, such as Augmented Reality, shape memory muscle wire, and wearable projection. Preliminary user feedback of our prototypes highlights the trade-offs between the benefits of sharing device activity and the need to protect user privacy.

Due to their limited input area, ultra-small devices, such as smartwatches, are even more prone to occlusion or the fat finger problem, than their larger counterparts, such as smart phones, tablets, and tabletop displays. We present NanoStylus -- a finger-mounted fine-tip stylus that enables fast and accurate pointing on a smartwatch with almost no occlusion. The NanoStylus is built from the circuitry of an active capacitive stylus, and mounted within a custom 3D-printed thimble-shaped housing unit. A sensor strip is mounted on each side of the device to enable additional gestures. A user study shows that NanoStylus reduces error rate by 80%, compared to traditional touch interaction and by 45%, compared to a traditional stylus. This high precision pointing capability, coupled with the implemented gesture sensing, gives us the opportunity to explore a rich set of interactive applications on a smartwatch form factor.

Task-based analysis is a common and effective way to measure expertise levels of software users. However, such assessments typically require in-person laboratory studies and inherently require knowledge of the user's task. Today, there is no accepted method for assessing a user's expertise levels outside of a lab, during a user's own home or work environment activities. In this article, we explore the feasibility of software applications automatically inferring a user's expertise levels, based on the user's in situ usage patterns. We outline the potential usage metrics that may be indicative of expertise levels and then perform a study, where we capture such metrics, by installing logging software in the participants' own workplace environments. We then invite those participants into a laboratory study and perform a more traditional task-based assessment of expertise. Our analysis of the study examines if metrics captured in situ, without any task knowledge, can be indicative of user expertise levels. The results show the existence of significant correlations between metrics calculated from in situ usage logs, and task-based user expertise assessments from our laboratory study. We discuss the implications of the results and how future software applications may be able to measure and leverage knowledge of the expertise of its users.

In 2009 we presented the idea of using collaborative filtering within a complex software application to help users learn new and relevant commands (Matejka et al. 2009). This project continued to evolve and we explored the design space of a contextual software command recommender system and completed a four-week user study (Li et al. 2011). We then expanded the scope of our project by implementing CommunityCommands, a fully functional and deployable recommender system. CommunityCommands was made available as a publically available plug-in download for Autodesk‟s flagship software application AutoCAD. During a one-year period, the recommender system was used by more than 1100 AutoCAD users. In this paper, we present our system usage data and payoff. We also provide an in-depth discussion of the challenges and design issues associated with developing and deploying the front end AutoCAD plug-in and its back end system. This includes a detailed description of the issues surrounding cold start and privacy. We also discuss how our practical system architecture was designed to leverage Autodesk‟s existing Customer Involvement Program (CIP) data to deliver in-product contextual recommendations to endusers. Our work sets important groundwork for the future development of recommender systems within the domain of end-user software learning assistance.

We present CADament, a gamified multiplayer tutorial system for learning AutoCAD. Compared with existing gamified software tutorial systems, CADament generates engaging learning experience through competitions. We investigate two variations of our game, where over-the-shoulder learning was simulated by providing viewports into other player's screens. We introduce an empirical lab study methodology where participants compete with one another, and we study knowledge transfer effects by tracking the migration of strategies between players during the study session. Our study shows that CADament has an advantage over pre-authored tutorials for improving learners' performance, increasing motivation, and stimulating knowledge transfer.

The emergence of smart devices (e.g., smart watches and smart eyewear) is redefining mobile interaction from the solo performance of a smart phone, to a symphony of multiple devices. In this paper, we present Duet -- an interactive system that explores a design space of interactions between a smart phone and a smart watch. Based on the devices' spatial configurations, Duet coordinates their motion and touch input, and extends their visual and tactile output to one another. This transforms the watch into an active element that enhances a wide range of phone-based interactive tasks, and enables a new class of multi-device gestures and sensing techniques. A technical evaluation shows the accuracy of these gestures and sensing techniques, and a subjective study on Duet provides insights, observations, and guidance for future work.

3D modelers often wish to showcase their models for sharing or review purposes. This may consist of generating static viewpoints of the model or authoring animated fly-throughs. Manually creating such views is often tedious and few automatic methods are designed to interactively assist the modelers with the view authoring process. We present a view authoring assistance system that supports the creation of informative view points, view paths, and view surfaces, allowing modelers to author the interactive navigation experience of a model. The key concept of our implementation is to analyze the model's workflow history, to infer important regions of the model and representative viewpoints of those areas. An evaluation indicated that the viewpoints generated by our algorithm are comparable to those manually selected by the modeler. In addition, participants of a user study found our system easy to use and effective for authoring viewpoint summaries.

We present Draco, a sketch-based interface that allows artists and casual users alike to add a rich set of animation effects to their drawings, seemingly bringing illustrations to life. While previous systems have introduced sketch-based animations for individual objects, our contribution is a unified framework of motion controls that allows users to seamlessly add coordinated motions to object collections. We propose a framework built around kinetic textures, which provide continuous animation effects while preserving the unique timeless nature of still illustrations. This enables many dynamic effects difficult or not possible with previous sketch-based tools, such as a school of fish swimming, tree leaves blowing in the wind, or water rippling in a pond. We describe our implementation and illustrate the repertoire of animation effects it supports. A user study with professional animators and casual users demonstrates the variety of animations, applications and creative possibilities our tool provides.

Short video demonstrations are effective resources for helping users to learn tools in feature-rich software. However manually creating demonstrations for the hundreds (or thousands) of individual features in these programs would be impractical. In this paper, we investigate the potential for identifying good tool demonstrations from within screen recordings of users performing real-world tasks. Using an instrumented image-editing application, we collected workflow video content and log data from actual end users. We then developed a heuristic for identifying demonstration clips, and had the quality of a sample set of clips evaluated by both domain experts and end users. This multi-step approach allowed us to characterize the quality of 'naturally occurring' tool demonstrations, and to derive a list of good and bad features of these videos. Finally, we conducted an initial investigation into using machine learning techniques to distinguish between good and bad demonstrations.

Ultra-small smart devices, such as smart watches, have become increasingly popular in recent years. Most of these devices rely on touch as the primary input modality, which makes tasks such as text entry increasingly difficult as the devices continue to shrink. In the sole pursuit of entry speed, the ultimate solution is a shorthand technique (e.g., Morse code) that sequences tokens of input (e.g., key, tap, swipe) into unique representations of each character. However, learning such techniques is hard, as it often resorts to rote memory. Our technique, Swipeboard, leverages our spatial memory of a QWERTY keyboard to learn, and eventually master a shorthand, eyes-free text entry method designed for ultra-small interfaces. Characters are entered with two swipes; the first swipe specifies the region where the character is located, and the second swipe specifies the character within that region. Our study showed that with less than two hours' training, Tested on a reduced word set, Swipeboard users achieved 19.58 words per minute (WPM), 15% faster than an existing baseline technique.

We present Kitty, a sketch-based tool for authoring dynamic and interactive illustrations. Artists can sketch animated drawings and textures to convey the living phenomena, and specify the functional relationship between its entities to characterize the dynamic behavior of systems and environments. An underlying graph model, customizable through sketching, captures the functional relationships between the visual, spatial, temporal or quantitative parameters of its entities. As the viewer interacts with the resulting dynamic interactive illustration, the parameters of the drawing change accordingly, depicting the dynamics and chain of causal effects within a scene. The generality of this framework makes our tool applicable for a variety of purposes, including technical illustrations, scientific explanation, infographics, medical illustrations, children's e-books, cartoon strips and beyond. A user study demonstrates the ease of usage, variety of applications, artistic expressiveness and creative possibilities of our tool.

We present Video Lens, a framework which allows users to visualize and interactively explore large collections of videos and associated metadata. The primary goal of the framework is to let users quickly find relevant sections within the videos and play them back in rapid succession. The individual UI elements are linked and highly interactive, supporting a faceted search paradigm and encouraging exploration of the data set. We demonstrate the capabilities and specific scenarios of Video Lens within the domain of professional baseball videos. A user study with 12 participants indicates that Video Lens efficiently supports a diverse range of powerful yet desirable video query tasks, while a series of interviews with professionals in the field demonstrates the framework's benefits and future potential.

3D printers offer extraordinary flexibility for prototyping the shape and mechanical function of objects. We investigate how 3D models can be modified to facilitate the creation of interactive objects that offer dynamic input and output. We introduce a general technique for supporting the rapid prototyping of interactivity by removing interior material from 3D models to form internal pipes. We describe this new design space of pipes for interaction design, where variables include openings, path constraints, topologies, and inserted media. We then present PipeDream, a tool for routing such pipes through the interior of 3D models, integrated within a 3D modeling program. We use two distinct routing algorithms. The first has users define pipes' terminals, and uses path routing and physics-based simulation to minimize pipe bending energy, allowing easy insertion of media post-print. The second allows users to supply a desired internal shape to which we fit a pipe route: for this we describe a graph-routing algorithm. We present several prototypes created using our tool to show its flexibility and potential.

One of the fundamental operations in today's user interfaces is pointing to targets, such as menus, buttons, and text. Making an error when selecting those targets in real-life user interfaces often results in some cost to the user. However, the existing target-directed pointing models do not consider the cost of error when predicting task completion time. In this paper, we present a model based on expected value theory that predicts the impact of the error cost on the user's completion time for target-directed pointing tasks. We then present a target-directed pointing user study, which results show that time-based costs of error significantly impact the user's performance. Our results also show that users perform according to an expected completion time utility function and that optimal performance computed using our model gives good prediction of the observed task completion times.

We present a multi-site field study to evaluate LemonAid, a crowdsourced contextual help approach that allows users to retrieve relevant questions and answers by making selections within the interface. We deployed LemonAid on 4 different web sites used by thousands of users and collected data over several weeks, gathering over 1,200 usage logs, 168 exit surveys, and 36 one-on-one interviews. Our results indicate that over 70% of users found LemonAid to be helpful, intuitive, and desirable for reuse. Software teams found LemonAid easy to integrate with their sites and found the analytics data aggregated by LemonAid a novel way of learning about users' popular questions. Our work provides the first holistic picture of the adoption and use of a crowdsourced contextual help system and offers several insights into the social and organizational dimensions of implementing such help systems for real-world applications.

Web-based tutorials are a popular help resource for learning how to perform unfamiliar tasks in complex software. However, in their current form, web tutorials are isolated from the applications that they support. In this paper we present FollowUs, a web-tutorial system that integrates a fully-featured application into a web-based tutorial. This novel architecture enables community enhanced tutorials, which continuously improve as more users work with them. FollowUs captures video demonstrations of users as they perform a tutorial. Subsequent users can use the original tutorial, or choose from a library of captured community demonstrations of each tutorial step. We conducted a user study to test the benefits of making multiple demonstrations available to users, and found that users perform significantly better using our system with a library of multiple demonstrations in comparison to its equivalent baseline system with only the original authored content.

Online streaming video systems have become extremely popular, yet navigating to target scenes of interest can be a challenge. While recent techniques have been introduced to enable real-time seeking, they break down for large videos, where scrubbing the timeline causes video frames to skip and flash too quickly to be comprehendible. We present Swifter, a new video scrubbing technique that displays a grid of pre-cached thumbnails during scrubbing actions. In a series of studies, we first investigate possible design variations of the Swifter technique, and the impact of those variations on its performance. Guided by these results we compare an implementation of Swifter to the previously published Swift technique, in addition to the approaches utilized by YouTube and Netfilx. Our results show that Swifter significantly outperforms each of these techniques in a scene locating task, by a factor of up to 48%.

We present Patina, an application independent system for collecting and visualizing software application usage data. Patina requires no instrumentation of the target application, all data is collected through standard window metrics and accessibility APIs. The primary visualization is a dynamic heatmap overlay which adapts to match the content, location, and shape of the user interface controls visible in the active application. We discuss a set of design guidelines for the Patina system, describe our implementation of the system, and report on an initial evaluation based on a short-term deployment of the system.

YouMove is a novel system that allows users to record and learn physical movement sequences. The recording system is designed to be simple, allowing anyone to create and share training content. The training system uses recorded data to train the user using a large-scale augmented reality mirror. The system trains the user through a series of stages that gradually reduce the user's reliance on guidance and feedback. This paper discusses the design and implementation of YouMove and its interactive mirror. We also present a user study in which YouMove was shown to improve learning and short-term retention by a factor of 2 compared to a traditional video demonstration.

Applications typically provide ways for expert users to increase their performance, such as keyboard shortcuts or customization, but these facilities are frequently ignored. To help address this problem, we introduce skillometers -- lightweight displays that visualize the benefits available through practicing, adopting a better technique, or switching to a faster mode of interaction. We present a general framework for skillometer design, then discuss the design and implementation of a real-world skillometer intended to increase hotkey use. A controlled experiment shows that our skillometer successfully encourages earlier and faster learning of hotkeys. Finally, we discuss general lessons for future development and deployment of skillometers.

General-purpose software applications are usually not tailored for a specific user with specific tasks, strategies or preferences. In order to achieve optimal performance with such applications, users typically need to transition to an alternative efficient behavior. Often, features of such alternative behaviors are not initially accessible and first need to be customized. However, few research works formally study and empirically measure what drives a user to customize. In this paper, we describe the challenges involved in empirically studying customization behaviors, and propose a methodology for formally measuring the impact of potential customization factors. We then demonstrate this methodology by studying the impact of different customization factors on customization behaviors. Our results show that increasing exposure and awareness of customization features, and adding social influence can significantly affect the user's customization behavior.

We investigate implanted user interfaces that small devices provide when implanted underneath human skin. Such devices always stay with the user, making their implanted user interfaces available at all times. We discuss four core challenges of implanted user interfaces: how to sense input through the skin, how to produce output, how to communicate amongst one another and with external infrastructure, and how to remain powered. We investigate these four challenges in a technical evaluation where we surgically implant study devices into a specimen arm. We find that traditional interfaces do work through skin. We then demonstrate how to deploy a prototype device on participants, using artificial skin to simulate implantation. We close with a discussion of medical considerations of implanted user interfaces, risks and limitations, and project into the future.

Tutorials and sample workflows for complicated, feature-rich software packages are widely available online. As a result users must differentiate between workflows to choose the most suitable one for their task. We present Delta, an interactive workflow visualization and comparison tool that helps users identify the tradeoffs between workflows. We conducted an initial study to identify the set of attributes users attend to when comparing workflows, finding that they consider result quality, their knowledge of commands, and the efficiency of the workflow. We then designed Delta to surface these attributes at three granularities: a high-level, clustered view; an intermediate-level list view that contains workflow summaries; and a low-level detail view that allows users to compare two individual workflows. Finally, we conducted an evaluation of Delta on a small corpus of 30 workflows and found that the intermediate list view provided the best information density. We conclude with thoughts on how such a workflow comparison system could be scaled up to larger corpora in the future.

We first conduct a study using abstracted video content to measure the effects of latency on video scrubbing performance and find that even very small amounts of latency can significantly degrade navigation performance. Based on these results, we present Swift, a technique that supports real-time scrubbing of online videos by overlaying a small, low resolution copy of the video during video scrubbing, and snapping back to the high resolution video when the scrubbing is completed or paused. A second study compares the Swift technique to traditional online video players on a collection of realistic live motion videos and content-specific search tasks which finds the Swift technique reducing completion times by as much as 72% even with a relatively low latency of 500ms. Lastly, we demonstrate that the Swift technique can be easily implemented using modern HTML5 web standards.

We present Waken, an application-independent system that recognizes UI components and activities from screen captured videos, without any prior knowledge of that application. Waken can identify the cursors, icons, menus, and tooltips that an application contains, and when those items are used. Waken uses frame differencing to identify occurrences of behaviors that are common across graphical user interfaces. Candidate templates are built, and then other occurrences of those templates are identified using a multi-phase algorithm. An evaluation demonstrates that the system can successfully reconstruct many aspects of a UI without any prior application-dependant knowledge. To showcase the design opportunities that are introduced by having this additional meta-data, we present the Waken Video Player, which allows users to directly interact with UI components that are displayed in the video.

We present GamiCAD, a gamified in-product, interactive tutorial system for first time AutoCAD users. We introduce a software event driven finite state machine to model a user's progress through a tutorial, which allows the system to provide real-time feedback and recognize success and failures. GamiCAD provides extensive real-time visual and audio feedback that has not been explored before in the context of software tutorials. We perform an empirical evaluation of GamiCAD, comparing it to an equivalent in-product tutorial system without the gamified components. In an evaluation, users using the gamified system reported higher subjective engagement levels and performed a set of testing tasks faster with a higher completion ratio.

We present Magic Finger, a small device worn on the fingertip, which supports always-available input. Magic Finger inverts the typical relationship between the finger and an interactive surface: with Magic Finger, we instrument the user's finger itself, rather than the surface it is touching. Magic Finger senses touch through an optical mouse sensor, enabling any surface to act as a touch screen. Magic Finger also senses texture through a micro RGB camera, allowing contextual actions to be carried out based on the particular surface being touched. A technical evaluation shows that Magic Finger can accurately sense 22 textures with an accuracy of 98.9%. We explore the interaction design space enabled by Magic Finger, and implement a number of novel interaction techniques that leverage its unique capabilities.

Despite the prominence of multi-touch technologies, there has been little work investigating its integration into the desktop environment. Bringing multi-touch into desktop computing would give users an additional input channel to leverage, enriching the current interaction paradigm dominated by a mouse and keyboard. We provide two main contributions in this domain. First, we describe the results from a study we performed, which systematically evaluates the various potential regions within the traditional desktop configuration that could become multi-touch enabled. The study sheds light on good or bad regions for multi-touch, and also the type of input most appropriate for each of these regions. Second, guided by the results from our study, we explore the design space of multi-touch-integrated desktop experiences. A set of new interaction techniques are coherently integrated into a desktop prototype, called Magic Desk, demonstrating potential uses for multi-touch enabled desktop configurations.

Despite being an important channel for end-user assistance, few studies have directly investigated the interactions that occur in modern-day practice of software product support. We present results from a multi-dimensional analysis of product support activities at a leading design software company. We carried out a quantitative analysis of existing support requests, a survey with product support specialists, and follow-up interviews to understand the current practices in product support. In particular, we investigated the utility of different multimedia formats that modern web-based support systems enable. Our results showed that despite the value that these formats bring to support tasks, support specialists still face bottlenecks in remotely resolving software problems. We conclude by highlighting several opportunities in HCI for improving diagnosis and resolution of software issues over the web.

Despite the growing research on usability in the pre-development phase, we know little about post-deployment usability activities. To characterize these activities, we surveyed 333 full-time usability professionals and consultants working in large and small corporations from a wide range of industries. Our results show that, as a whole, usability professionals are currently not playing a substantial role in the post-deployment phase compared to other phases of user-centered design, but when they do, practitioners find their interactions quite valuable. We highlight opportunities in HCI research and practice to bridge this gap by working more closely with software support and maintenance teams. We also raise the need to understand what might be called 'usability maintenance,' that is, the process and procedures, by which usability is maintained after deployment.

Numerous applications such as simulations, air traffic control systems, and video surveillance systems are inherently composed of spatial objects that move in a scene. In many instances, users can benefit from tools that allow them to select these targets in real-time, without having to pause the dynamic display. However, selecting moving objects is considerably more difficult and error prone than selecting stationary targets. In this paper, we evaluate the effectiveness of several techniques that assist in selecting moving targets. We present Comet, a technique that enhances targets based on their speed and direction. We also introduce Target Ghost, which allows users to select a static proxy of the target, while leaving the motion uninterrupted. We found a speed benefit for the Comet in a 1D selection task in comparison to other cursor and target enhancements. For 2D selection, Comet outperformed Bubble cursor but only when Target Ghost was not available. We conclude with guidelines for design.

In this paper we present Ambient Help, a system that supports opportunistic learning by providing automatic, context-sensitive learning resources while a user works. Multiple videos and textual help resources are presented ambiently on a secondary display. We define and examine a collection of design consideration for this type of interface. After describing our implementation details, we report on an experiment which shows that Ambient Help supports finding more helpful information, while not having a negative impact on the user's productivity, as compared to a traditional help condition.

Although touch-screen laptops are increasing in popularity, users still do not comfortably rely on touch in these environments, as current software interfaces were not designed for being used by the finger. In this paper, we first demonstrate the benefits of using touch as a complementary input modality along with the keyboard and mouse or touchpad in a laptop setting. To alleviate the frustration users experience with touch, we then design two techniques, TouchCuts, a single target expansion technique, and ,i>TouchZoom,/i>, a multiple target expansion technique. Both techniques facilitate the selection of small icons, by detecting the finger proximity above the display surface, and expanding the target as the finger approaches. In a controlled evaluation, we show that our techniques improve performance in comparison to both the computer mouse and a baseline touch-based target acquisition technique. We conclude by discussing other application scenarios that our techniques support.

We present Medusa, a proximity-aware multi-touch tabletop. Medusa uses 138 inexpensive proximity sensors to: detect a user's presence and location, determine body and arm locations, distinguish between the right and left arms, and map touch point to specific users and specific hands. Our tracking algorithms and hardware designs are described. Exploring this unique design, we develop and report on a collection of interactions enabled by Medusa in support of multi-user collaborative design, specifically within the context of Proxi-Sketch, a multi-user UI prototyping tool. We discuss design issues, system implementation, limitations, and generalizable concepts throughout the paper.

Users of complex software applications frequently need to consult documentation, tutorials, and support resources to learn how to use the software and further their understand-ing of its capabilities. Existing online help systems provide limited context awareness through "what's this?" and simi-lar techniques. We examine the possibility of making more use of the user's current context in a particular application to provide useful help resources. We provide an analysis and taxonomy of various aspects of application context and how they may be used in retrieving software help artifacts with web browsers, present the design of a context-aware augmented web search system, and describe a prototype implementation and initial user study of this system. We conclude with a discussion of open issues and an agenda for further research.

We describe Sketch-Sketch Revolution, a new tutorial system that allows any user to experience the success of drawing content previously created by an expert artist. Sketch-Sketch Revolution not only guides users through the application user interface, it also provides assistance with the actual sketching. In addition, the system offers an authoring tool that enables artists to create content and then automatically generates a tutorial from their recorded workflow history. Sketch-Sketch Revolution is a unique hybrid tutorial system that combines in-product, content-centric and reactive tutorial methods to provide an engaging learning experience. A qualitative user study showed that our system successfully taught users how to interact with a drawing application user interface, gave users confidence they could recreate expert content, and was uniformly considered useful and easy to use.

We describe TwitApp, an enhanced micro-blogging system integrated within AutoCAD for design sharing. TwitApp integrates rich content and still keeps the sharing transaction cost low. In TwitApp, tweets are organized by their project, and users can follow or unfollow each individual project. We introduce the concept of automatic tweet drafting and other novel features such as enhanced real-time search and integrated live video streaming. The TwitApp system leverages the existing Twitter micro-blogging system. We also contribute a study which provides insights on these concepts and associated designs, and demonstrates potential user excitement of such tools.

We present IP-QAT, a new community-based question and answer system for software users. Unlike most community forums, IP-QAT is integrated into the actual software application, allowing users to easily post questions, answers and tips without having to leave the application. Our in-product implementation is context-aware and shows relevant posts based on a user's recent activity. It is also designed with minimal transaction costs to encourage users to easily post, include annotated images and file attachments, as well as tag their posts with relevant UI components. We describe a robust cloud-based system implementation, which allowed us to release IP-QAT to 37 users for a 2 week field study. Our study showed that IP-QAT increased user contributions, and subjectively, users found our system more useful and easier to use, in comparison to the existing commercial discussion board.

We examine the use of modern recommender system technology to aid command awareness in complex software applications. We first describe our adaptation of traditional recommender system algorithms to meet the unique requirements presented by the domain of software commands. A user study showed that our item-based collaborative filtering algorithm generates 2.1 times as many good suggestions as existing techniques. Motivated by these positive results, we propose a design space framework and its associated algorithms to support both global and contextual recommendations. To evaluate the algorithms, we developed the CommunityCommands plug-in for AutoCAD. This plug-in enabled us to perform a 6-week user study of real-time, within-application command recommendations in actual working environments. We report and visualize command usage behaviors during the study, and discuss how the recommendations affected users behaviors. In particular, we found that the plug-in successfully exposed users to new commands, as unique commands issued significantly increased.

Recently, research in 3D computer graphics and interaction has started to move beyond the narrow domain of single object authoring and inspection, and has begun to consider complex multiscale objects and environments. This generalization of problem scope calls for more general solutions, which are more akin to information visualization techniques than traditional computer graphics approaches.

We investigate the use of on-line contextual video assistance to improve the learnability of software functionality. After discussing motivations and design goals for such forms of assistance, we present our new technique, ToolClips. ToolClips augment traditional tooltips to provide users with quick and contextual access to both textual and video assistance. In an initial study we found that users successfully integrated ToolClip usage into the flow of their primary tasks to overcome learnability difficulties. In a second study, we found that with ToolClips, users successfully completed 7 times as many unfamiliar tasks, in comparison to using a commercial professionally developed on-line help system. Users also retained the information obtained from ToolClips, performing tasks significantly faster one week later.

Despite the considerable quantity of research directed towards multitouch technologies, a set of standardized UI components have not been developed. Menu systems provide a particular challenge, as traditional GUI menus require a level of pointing precision inappropriate for direct finger input. Marking menus are a promising alternative, but have yet to be investigated or adapted for use within multitouch systems. In this paper, we first investigate the human capabilities for performing directional chording gestures, to assess the feasibility of multitouch marking menus. Based on the positive results collected from this study, and in particular, high angular accuracy, we discuss our new multitouch marking menu design, which can increase the number of items in a menu, and eliminate a level of depth. A second experiment showed that multitouch marking menus perform significantly faster than traditional hierarchal marking menus, reducing acquisition times in both novice and expert usage modalities.

MouseLight is a spatially-aware standalone mobile projector with the form factor of a mouse that can be used in combination with digital pens on paper. By interacting with the projector and the pen bimanually, users can visualize and modify the virtually augmented contents on top of the paper, and seamlessly transition between virtual and physical information. We present a high fidelity hardware prototype of the system and demonstrate a set of novel interactions specifically tailored to the unique properties of MouseLight. MouseLight differentiates itself from related systems such as PenLight in two aspects. First, MouseLight presents a rich set of bimanual interactions inspired by the ToolGlass interaction metaphor, but applied to physical paper. Secondly, our system explores novel displaced interactions, that take advantage of the independent input and output that is spatially aware of the underneath paper. These properties enable users to issue remote commands such as copy and paste or search. We also report on a preliminary evaluation of the system which produced encouraging observations and feedback.

We describe Chronicle, a new system that allows users to explore document workflow histories. Chronicle captures the entire video history of a graphical document, and provides links between the content and the relevant areas of the history. Users can indicate specific content of interest, and see the workflows, tools, and settings needed to reproduce the associated results, or to better understand how it was constructed to allow for informed modification. Thus, by storing the rich information regarding the document's history workflow, Chronicle makes any working document a potentially powerful learning tool. We outline some of the challenges surrounding the development of such a system, and then describe our implementation within an image editing application. A qualitative user study produced extremely encouraging results, as users unanimously found the system both useful and easy to use.

It is well-accepted that learnability is an important aspect of usability, yet there is little agreement as to how learnability should be defined, measured, and evaluated. In this paper, we present a survey of the previous definitions, metrics, and evaluation methodologies which have been used for software learnability. Our survey of evaluation methodologies leads us to a new question-suggestion protocol, which, in a user study, was shown to expose a significantly higher number of learnability issues in comparison to a more traditional think-aloud protocol. Based on the issues identified in our study, we present a classification system of learnability issues, and demonstrate how these categories can lead to guidelines for addressing the associated challenges.

We explore the use of multi-finger input to emulate full mouse functionality, such as the tracking state, three buttons, and chording. We first present the design space for such techniques, which serves as a guide for the systematic investigation of possible solutions. We then perform a series of pilot studies to come up with recommendations for the various aspects of the design space. These pilot studies allow us to arrive at a recommended technique, the SDMouse. In a formal study, the SDMouse was shown to significantly improve performance in comparison to previously developed mouse emulation techniques.

Digital pen systems, originally designed to digitize annotations made on physical paper, are evolving to permit a wider variety of applications. Although the type and quality of pen feedback (e.g., haptic, audio, and visual) have a huge impact on advancing the digital pen technology, dynamic visual feedback has yet to be fully investigated. In parallel, miniature projectors are an emerging technology with the potential to enhance visual feedback for small mobile computing devices. In this paper we present the PenLight system, which is a testbed to explore the interaction design space and its accompanying interaction techniques in a digital pen embedded with a spatially-aware miniature projector. Using our prototype, that simulates a miniature projection (via a standard video projector), we visually augment paper documents, giving the user immediate access to additional information and computational tools. We also show how virtual ink can be managed in single and multi-user environments to aid collaboration and data management. User evaluation with professional architects indicated promise of our proposed techniques and their potential utility in the paper-intensive domain of architecture.

We explore the use of modern recommender system technology to address the problem of learning software applications. Before describing our new command recommender system, we first define relevant design considerations. We then discuss a 3 month user study we conducted with professional users to evaluate our algorithms which generated customized recommendations for each user. Analysis shows that our item-based collaborative filtering algorithm generates 2.1 times as many good suggestions as existing techniques. In addition we present a prototype user interface to ambiently present command recommendations to users, which has received promising initial user feedback.

Volumetric displays possess a number of unique properties which potentially make them particularly suitable for collaborative 3D applications. Because such displays have only recently become available, interaction techniques for collaborative usage have yet to be explored. In this paper, we initiate this exploration. We present a prototype collaborative 3D model viewing application, which served as a platform for our explorations. We outline three design goals, discuss the key interaction issues which were encountered, and describe a suite of new techniques in detail. In initial user observation sessions, we found that our techniques allowed users to successfully complete a variety of 3D tasks. Furthermore, interviews with experts in potential usage domains indicated that the techniques we developed can serve as a baseline for future collaborative applications for volumetric displays.

This paper reports on an exploratory study of the effects of input configuration on group behavior and performance in a collaborative task performed by a collocated group using a large display. Twelve groups completed a mixed-motive negotiation task under two conditions: a single, shared mouse and one mouse per person. Results suggest that the multiple mouse condition allowed for more parallel work, but the quality of discussion was higher in the single mouse condition. Moreover, participants were more likely to act in their own best interest in the multiple mouse condition.

Hotkeys are extremely useful in leveraging expert performance, but learning them is a slow process. This paper investigates alternative menu designs that can motivate and help users remember associations between menu commands and hotkeys. Building upon previous work on paired-associate learning, we suggest that the transition to expert use can be accelerated by manipulating feedback and cost associated with menu selection. We evaluate five designs in a pilot study and then two of the most promising ones in a formal experiment, showing that the speed of hotkey learning can indeed be significantly increased with little modifications to the standard menu/hotkey paradigm.

We investigate pointing at graphical targets of arbitrary shapes. We first describe a previously proposed probabilistic Fitts' law model [7] which, unlike previous models that only account for rectangular targets, has the potential to handle arbitrary shapes. Three methods of defining the centers of arbitrarily shaped targets for use within the model are developed. We compare these methods of defining target centers, and validate the model using a pointing experiment in which the targets take on various shapes. Results show that the model can accurately account for the varying target shapes. We discuss the implications of our results to interface design.

Volumetric displays, which provide a 360° view of imagery illuminated in true 3D space, are a promising platform for interactive 3D applications. However, presenting text in volumetric displays can be a challenge, as the text may not be oriented towards the user. This is especially problematic with multiple viewers, as the text could, for example, appear forwards to one user, and backwards to another. In a first experiment we determined the effects of 3D rotations on text readability. Based on the results, we developed and evaluated a new technique which optimizes text orientation for multiple viewers. This technique provided 33% faster group reading times in a collaborative experimental task.

Interaction techniques that utilize the space above the display surface to extend the functionalities of digitized surfaces continue to emerge. In such techniques, movements are constrained by the bounds of a layer. In addition, constraints imposed on the direction of movement within the layer may be present. Despite the presence of such techniques, there is limited understanding of human capabilities for performing the required steering task. In this paper we study and model user performance when steering through constrained and unconstrained paths in above-the-surface layers. Through a series of experiments we validate the derivation and applicability of our proposed models.

We present an experiment that compares volumetric displays to existing 3D display techniques in three tasks that require users to perceive depth in 3D scenes. Because they generate imagery in true 3D space, volumetric displays allow viewers to use their natural physiological mechanisms for depth perception, without requiring special hardware such as head trackers or shutter glasses. However, it is unclear from the literature as to whether these displays are actually better than the status-quo for enabling the perception of 3D scenes, thus motivating the present study. Our results show that volumetric displays enable significantly better user performance in a simple depth judgment task, and better performance in a collision judgment task, but in its current form does not enhance user comprehension of more complex 3D scenes.

We present Hover Widgets, a new technique for increasing the capabilities of pen-based interfaces. Hover Widgets are implemented by using the pen movements above the display surface, in the tracking state. Short gestures while hovering, followed by a pen down, access the Hover Widgets, which can be used to activate localized interface widgets. By using the tracking state movements, Hover Widgets create a new command layer which is clearly distinct from the input layer of a pen interface. In a formal experiment Hover Widgets were found to be faster than a more traditional command activation technique, and also reduced errors due to divided attention.

Volumetric displays, which display imagery in true 3D space, are a promising platform for the display and manipulation of 3D data. To fully leverage their capabilities, appropriate user interfaces and interaction techniques must be designed. In this paper, we explore 3D selection techniques for volumetric displays. In a first experiment, we find a ray cursor to be superior to a 3D point cursor in a single target environment. To address the difficulties associated with dense target environments we design four new ray cursor techniques which provide disambiguation mechanisms for multiple intersected targets. Our techniques showed varied success in a second, dense target experiment. One of the new techniques, the depth ray, performed particularly well, significantly reducing movement time, error rate, and input device footprint in comparison to the 3D point cursor.

We present the bubble cursor - a new target acquisition technique based on area cursors. The bubble cursor improves upon area cursors by dynamically resizing its activation area depending on the proximity of surrounding targets, such that only one target is selectable at any time. We also present two controlled experiments that evaluate bubble cursor performance in 1D and 2D target acquisition tasks, in complex situations with multiple targets of varying layout densities. Results show that the bubble cursor significantly outperforms the point cursor and the object pointing technique [7], and that bubble cursor performance can be accurately modeled and predicted using Fitts' law.

We investigate and model two-dimensional pointing where the target distance and size vary as does the angle of movement. We first study the spread of hits in a rapid approximate pointing task at varied distances and movement angles. Consistent with the literature, our results show that the spread of hits along the movement direction deviate more than the spread of hits in the direction perpendicular to movement, and both spreads increase with distance. Based on the distribution of this spread of hits, we propose and validate a new probabilistic model that describes two-dimensional pointing. Unlike previous models, our model accounts for more variables of two-dimensional pointing and can be generalized to any target shape, size, orientation, location, and dimension. In contrast to previous work, which suggests that target height has minimal impact on performance when it is larger than the width, our results show that, even when height is greater than width, it can significantly impact movement time.

Volumetric displays provide interesting opportunities and challenges for 3D interaction and visualization, particularly when used in a highly interactive manner. We explore this area through the design and implementation of techniques for interactive direct manipulation of objects with a 3D volumetric display. Motion tracking of the user's fingers provides for direct gestural interaction with the virtual objects, through manipulations on and around the display's hemispheric enclosure. Our techniques leverage the unique features of volumetric displays, including a 360° viewing volume that enables manipulation from any viewpoint around the display, as well as natural and accurate perception of true depth information in the displayed 3D scene. We demonstrate our techniques within a prototype 3D geometric model building application.

We investigate pointing in true 3D environments where the target size varies in three spatial dimensions. We also study the effect of the user's physical movement angle on pointing performance. Results show that target size dimension along the primary axis of movement has a greater impact on performance than the other two dimensions. Movement angle also significantly affects performance, and changes the relative impact of the three target dimensions. Building upon recent results in the modeling of bivariate pointing, we propose and validate a new model that describes pointing at trivariate targets. This model also accounts for movement angle, and outperforms previously published models.

Volumetric displays provide interesting opportunities and challenges for 3D interaction and visualization, particularly when used in a highly interactive manner. We explore this area through the design and implementation of techniques for interactive direct manipulation of objects with a 3D volumetric display. Motion tracking of the user's fingers provides for direct gestural interaction with the virtual objects, through manipulations on and around the display's hemispheric enclosure. Our techniques leverage the unique features of volumetric displays, including a 360° viewing volume that enables manipulation from any viewpoint around the display, as well as natural and accurate perception of true depth information in the displayed 3D scene. We demonstrate our techniques within a prototype 3D geometric model building application.

Current interfaces for manipulating curves typically use a standard point cursor to indirectly adjust curve parameters. We present an interface for far more direct manipulation of curves using a specialized high degree-of-freedom curve input device, called ShapeTape. This device allows us to directly control the shape and position of a virtual curve widget. We describe the design and implementation of a variety of interaction techniques that use this curve widget to create and manipulate other virtual curves in 2D and 3D space. The input device is also used to sense a set of user gestures for invoking commands and tools. The result is an effective alternate user interface for curve manipulation that can be used in 2D and 3D graphics applications.

Previous systems have explored the challenges of designing an interface for automotive styling which combine the metaphor of 2D drawing using physical tape with the simultaneous creation and management of a corresponding virtual 3D model. These systems have been limited to only 2D planar curves while typically the principal characteristic curves of an automotive design are three dimensional and non-planar. We present a system which addresses this limitation. Our system allows a designer to construct these non-planar 3D curves by drawing a series of 2D curves using the 2D tape drawing technique and interaction style. These results are generally applicable to the interface design of 3D modeling applications and also to the design of arm's length interaction on large scale display systems

We present an alternate interface for 3D modeling for use on large scale displays. The interface integrates several concepts specifically selected and enhanced for large scale interaction. These include 2D construction planes spatially integrated in a 3D volume, enhanced orthographic views, smooth transitions between 2D and 3D views, tape drawing as the primary curve and line creation technique, visual viewpoint markers, and continuous two-handed interaction.